{"cells":[{"cell_type":"markdown","metadata":{"id":"jwqDXW4wwJ5O"},"source":["**Note**\n","\n","This notebook contains only preprocessing, model building and prediction codes. All the ML experiements were carried out in a separate notebook"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14983,"status":"ok","timestamp":1627872428975,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"eDbgWgR0wSkz","outputId":"35c18d92-323c-4cca-a278-362c3bff23dc"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: category_encoders in /usr/local/lib/python3.7/dist-packages (2.2.2)\n","Requirement already satisfied: scikit-learn\u003e=0.20.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.22.2.post1)\n","Requirement already satisfied: numpy\u003e=1.14.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.19.5)\n","Requirement already satisfied: statsmodels\u003e=0.9.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.10.2)\n","Requirement already satisfied: patsy\u003e=0.5.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (0.5.1)\n","Requirement already satisfied: pandas\u003e=0.21.1 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.1.5)\n","Requirement already satisfied: scipy\u003e=1.0.0 in /usr/local/lib/python3.7/dist-packages (from category_encoders) (1.4.1)\n","Requirement already satisfied: python-dateutil\u003e=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas\u003e=0.21.1-\u003ecategory_encoders) (2.8.1)\n","Requirement already satisfied: pytz\u003e=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas\u003e=0.21.1-\u003ecategory_encoders) (2018.9)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy\u003e=0.5.1-\u003ecategory_encoders) (1.15.0)\n","Requirement already satisfied: joblib\u003e=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn\u003e=0.20.0-\u003ecategory_encoders) (1.0.1)\n","Collecting catboost\n","  Downloading catboost-0.26-cp37-none-manylinux1_x86_64.whl (69.2 MB)\n","\u001b[K     |████████████████████████████████| 69.2 MB 5.1 kB/s \n","\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n","Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n","Requirement already satisfied: numpy\u003e=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n","Requirement already satisfied: pandas\u003e=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n","Requirement already satisfied: pytz\u003e=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas\u003e=0.24.0-\u003ecatboost) (2018.9)\n","Requirement already satisfied: python-dateutil\u003e=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas\u003e=0.24.0-\u003ecatboost) (2.8.1)\n","Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,\u003e=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib-\u003ecatboost) (2.4.7)\n","Requirement already satisfied: kiwisolver\u003e=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib-\u003ecatboost) (1.3.1)\n","Requirement already satisfied: cycler\u003e=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib-\u003ecatboost) (0.10.0)\n","Requirement already satisfied: retrying\u003e=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly-\u003ecatboost) (1.3.3)\n","Installing collected packages: catboost\n","Successfully installed catboost-0.26\n"]}],"source":["!pip install category_encoders\n","!pip install catboost"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":385,"status":"ok","timestamp":1627872431925,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"_2a_sFx7wJ5Q"},"outputs":[],"source":["#Import all libraries\n","\n","import pandas as pd\n","import numpy as np\n","import category_encoders as ce\n","\n","import pandas as pd\n","import numpy as np\n","import category_encoders as ce\n","\n","from sklearn.metrics import mean_squared_log_error , make_scorer\n","from sklearn.preprocessing import StandardScaler, MinMaxScaler , RobustScaler , power_transform , PowerTransformer, KBinsDiscretizer\n","from xgboost import XGBRegressor\n","from sklearn.model_selection import train_test_split\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.ensemble import RandomForestRegressor\n","from sklearn.ensemble import ExtraTreesRegressor\n","from catboost import CatBoostRegressor\n","from sklearn.ensemble import StackingRegressor\n","\n","from sklearn.model_selection import KFold, cross_val_score , RepeatedStratifiedKFold \n","from sklearn.model_selection import GridSearchCV\n","from sklearn import preprocessing\n","from sklearn.feature_selection import RFE , RFECV\n","from sklearn.model_selection import RepeatedKFold\n","from sklearn.neural_network import MLPRegressor\n","from sklearn.feature_selection import SelectKBest\n","from sklearn.preprocessing import QuantileTransformer\n","from sklearn.model_selection import RandomizedSearchCV\n","\n","from sklearn.svm import SVR\n","from sklearn.pipeline import make_pipeline\n","\n","pd.set_option('display.max_rows', None)\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","import warnings\n","warnings.filterwarnings(\"ignore\")\n","\n","from sklearn.cluster import KMeans\n","\n","import math"]},{"cell_type":"code","execution_count":8,"metadata":{"executionInfo":{"elapsed":343,"status":"ok","timestamp":1627872556348,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"1UVgT5PRwik4"},"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/Saideepak_1st_Place_MATHCO.Thon.zip (Unzipped Files)/Saideepak_1st_Place_MATHCO.Thon')"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17095,"status":"ok","timestamp":1627872499292,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"T9G71xPbwlMI","outputId":"658e9461-c1c6-4fb3-d31d-29ab81f5e0aa"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"avt55n0Kw9Lk"},"outputs":[],"source":[""]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":1662,"status":"ok","timestamp":1627872561590,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"oWHs3O-JwJ5R"},"outputs":[],"source":["#Read train and test data\n","\n","train = pd.read_csv('train.csv')\n","test = pd.read_csv('test.csv')"]},{"cell_type":"code","execution_count":10,"metadata":{"executionInfo":{"elapsed":354,"status":"ok","timestamp":1627872563759,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"cluemThpwJ5S"},"outputs":[],"source":["def preprocess(data):\n","    \n","    # Removed strings present in the feature 'Mileage' using  lambda function\n","    data['Mileage'] = data['Mileage'].apply(lambda x: x.split(\" \")[0]).astype(int)\n","   \n","    #Log transformation is applied on the feature 'Mileage'\n","    data['Mileage'] = data['Mileage'].apply(lambda x: np.log(1) if x == 0 else np.log(x) )\n","    \n","    #Null values are imputed with 1\n","    data['Levy'] = data['Levy'].apply(lambda x: 1 if x == '-' else x).astype(int)\n","    \n","    #All the rare values in the 'category' feature are combined together using lambda function\n","    data['Category'] = data['Category'].apply(lambda x: 'Heavy_vehicle' if x == 'Goods wagon' or x == 'Pickup'\n","                                              or x == 'Cabriolet' or x == 'Limousine' else x)\n","\n","    #Using the 'Engine volume' feature a new feature is created called 'Turbo engine'\n","    data['Turbo_engine'] = data['Engine volume'].apply(lambda x: 'Yes' if x.split(\" \")[-1] == 'Turbo' else 'No')\n","    data['Engine volume'] = data['Engine volume'].apply(lambda x: x.split(\" \")[0]).astype(float)\n","\n","    #A new feature called 'Car age' is created using 'Prod year'\n","    data['Car_age'] = 2021 - data['Prod. year']\n","    \n","    # 'Hybrid-hydrogen' and 'Hydrogen' are combined together in the 'fuel type' feature using lambda function\n","    data['Fuel type'] = data['Fuel type'].apply(lambda x: 'Hybrid-hydrogen' if x == 'Plug-in Hybrid' or x == 'Hydrogen' else x)\n","   \n","    \n","   #Log is applied on the feature 'Levy'\n","    data['Levy'] = np.log(data['Levy'])\n","  \n","    # Log is applied on the feature 'ID'\n","    data['ID'] = np.log(data['ID'])\n","   \n","\n","    #Below are the various feature engineering using mathematical function\n","    data['ID*Levy'] = np.log((data['ID'] * (data['Levy'] + data['ID'] )))\n","    data['ID*Mileage'] = np.log((data['ID'] * (data['Mileage'] + data['ID'] )))\n","    data['ID*Airbags'] = np.log((data['ID'] * (data['Airbags'] + data['ID'] )))\n","\n","    data['ID_inverse'] = 1/ data['ID']\n","    data['Mileage_inverse'] = 1/(data['Mileage'] + 0.5)\n","    data['sin_ID'] =  np.sin(data['ID'])\n","    data['sqrt_ID'] = np.sqrt(data['ID'])\n","    data['sqrt_mileage'] = np.sqrt(data['Mileage'] )\n","    data['sqrt_Levy'] = np.sqrt(data['Levy'])\n","    data['inverse_ID*mileage'] =   1/data['ID*Mileage']\n","    data['ID_inverse*sin_ID'] = data['ID_inverse']*data['sin_ID']\n","    return data"]},{"cell_type":"code","execution_count":11,"metadata":{"executionInfo":{"elapsed":465,"status":"ok","timestamp":1627872577668,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"BfV7ar4twJ5T"},"outputs":[],"source":["#Reading train data\n","x = train.drop('Price' , axis = 1)\n","\n","# Log is applied on the target feature for better prediction. Finally after prediction we ll apply exponential function\n","y = np.log(train['Price'])\n","\n","\n","#Reading test data\n","x_test = test.drop('Price' , axis = 1)"]},{"cell_type":"code","execution_count":12,"metadata":{"executionInfo":{"elapsed":382,"status":"ok","timestamp":1627872580910,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"EhTFveZXwJ5T"},"outputs":[],"source":["#Calling preprocess function and applying on train data\n","data = preprocess(x)"]},{"cell_type":"code","execution_count":13,"metadata":{"executionInfo":{"elapsed":574,"status":"ok","timestamp":1627872604017,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"1RxJBb_rwJ5T"},"outputs":[],"source":["#Fitting count encoder on the features 'Manufacturer' , 'Model' , 'Color' and 'Doors'\n","encoder_count = ce.CountEncoder(cols = ['Manufacturer' , 'Model' , 'Color' , 'Doors' ] , handle_unknown = 1)\n","df1 = encoder_count.fit_transform( data)\n","\n","\n","#Fitting one hot encoder on rest of the categorical features\n","encoder_onehot = ce.OneHotEncoder(cols= [ 'Category' , 'Fuel type' , 'Leather interior'  , 'Gear box type' , \n","                                          'Drive wheels' , 'Wheel' ,  'Turbo_engine'])\n","\n","train_final = encoder_onehot.fit_transform( df1)\n","\n","#Few more feature engineering is done using mathematical function\n","train_final['Inverse_manufacturer'] = 1/train_final['Manufacturer']\n","train_final['Inverse_model'] = 1/train_final['Model']\n","train_final['model*manufacturer'] = train_final['Model'] * train_final['Manufacturer']\n","train_final['sin_inverse*model*manufacturer'] = np.sin(train_final['Inverse_manufacturer'] * train_final['Inverse_model'])\n","train_final['sin*manufacturer'] = np.sin(train_final['Manufacturer'])\n","train_final['sin*model'] = np.cos(train_final['Model'])\n","train_final['mileage*model'] = train_final['Model'] * train_final['Mileage']\n","train_final['Color*manufacturer'] = train_final['Color'] * train_final['Manufacturer']\n","train_final['ID*model'] = train_final['ID'] * train_final['Model']\n","train_final['cos_ID'] = np.cos(train_final['ID'])"]},{"cell_type":"code","execution_count":14,"metadata":{"executionInfo":{"elapsed":332,"status":"ok","timestamp":1627872606745,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"M9SxnHKPwJ5U"},"outputs":[],"source":["#Train data is normalized using min max scaler for the purpose of doing clustering using KMeans algorithm\n","scaler = MinMaxScaler()\n","data_scaled = scaler.fit_transform(train_final)"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":1378,"status":"ok","timestamp":1627872614042,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"IItUNdojwJ5U"},"outputs":[],"source":["#KMeans algorithm is applied on the traind data\n","#Number of clusters is selected using elbow plot and it is not included in this notebook\n","\n","kmeans = KMeans(n_clusters=4, init='k-means++' , random_state = 1)\n","\n","# fitting the k means algorithm on scaled data\n","kmeans.fit(data_scaled)\n","pred = kmeans.predict(data_scaled)\n","train_final['cluster'] = pred\n","\n","encoder_onehot_cluster = ce.OneHotEncoder(cols= ['cluster'])\n","train_final = encoder_onehot_cluster.fit_transform(train_final)"]},{"cell_type":"code","execution_count":16,"metadata":{"executionInfo":{"elapsed":921,"status":"ok","timestamp":1627872619704,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"TFxOFxn9wJ5U"},"outputs":[],"source":["#Ouantile transformation is done on train data\n","#This reduces the impact of outliers in the prediction\n","qt = QuantileTransformer(random_state=123 , output_distribution = 'normal' , n_quantiles=1000)\n","\n","\n","train_final = qt.fit_transform(train_final)\n"]},{"cell_type":"markdown","metadata":{"id":"o91rstTYwJ5V"},"source":["#### The above process is applied to the test data as well"]},{"cell_type":"code","execution_count":17,"metadata":{"executionInfo":{"elapsed":385,"status":"ok","timestamp":1627872624950,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"EW9Om1ivwJ5V"},"outputs":[],"source":["#Preprocess function is applied on the test data\n","test_val = preprocess(x_test)"]},{"cell_type":"code","execution_count":18,"metadata":{"executionInfo":{"elapsed":558,"status":"ok","timestamp":1627872627487,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"V8Gq-0kcwJ5V"},"outputs":[],"source":["# All the fitted encoders are used for transforming the test data\n","test_set = encoder_count.transform(test_val)\n","\n","test_final = encoder_onehot.transform(test_set)\n","test_final['Inverse_manufacturer'] = 1/test_final['Manufacturer']\n","test_final['Inverse_model'] = 1/test_final['Model']\n","test_final['model*manufacturer'] = test_final['Model'] * test_final['Manufacturer']\n","test_final['sin_inverse*model*manufacturer'] = np.sin(test_final['Inverse_manufacturer'] * test_final['Inverse_model'])\n","test_final['sin*manufacturer'] = np.sin(test_final['Manufacturer'])\n","test_final['sin*model'] = np.cos(test_final['Model'])\n","test_final['mileage*model'] = test_final['Model'] * test_final['Mileage']\n","test_final['Color*manufacturer'] = test_final['Color'] * test_final['Manufacturer']\n","test_final['ID*model'] = test_final['ID'] * test_final['Model']\n","test_final['cos_ID'] = np.cos(test_final['ID'])\n","\n","scale_test = scaler.transform(test_final)\n","\n","test_final['cluster'] = kmeans.predict(scale_test)\n","test_final = encoder_onehot_cluster.transform(test_final)\n","\n","\n","\n","test_final = qt.transform(test_final)\n"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":354,"status":"ok","timestamp":1627872634010,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"_fYkxml-wJ5V","outputId":"35af5acb-b2be-4038-9591-08096133197d"},"outputs":[{"name":"stdout","output_type":"stream","text":["(19237, 64)\n","(8245, 64)\n"]}],"source":["print(train_final.shape)\n","print(test_final.shape)"]},{"cell_type":"markdown","metadata":{"id":"BPwIReQlwJ5W"},"source":["#### Model building"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":78828,"status":"ok","timestamp":1627872715008,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"jOl_vu3lwJ5W","outputId":"8b7b0825-b9cf-434a-c426-b1619cb9e2cd"},"outputs":[{"data":{"text/plain":["ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',\n","                    max_depth=45, max_features='auto', max_leaf_nodes=None,\n","                    max_samples=None, min_impurity_decrease=0.0,\n","                    min_impurity_split=None, min_samples_leaf=1,\n","                    min_samples_split=2, min_weight_fraction_leaf=0.0,\n","                    n_estimators=400, n_jobs=None, oob_score=False,\n","                    random_state=123, verbose=0, warm_start=False)"]},"execution_count":20,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["#Fitting extra tree regressor model on the train data\n","from sklearn.ensemble import ExtraTreesRegressor\n","etr = ExtraTreesRegressor(random_state = 123 , max_depth = 45  , n_estimators = 400)\n","etr.fit(train_final , y)"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":248682,"status":"ok","timestamp":1627872969332,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"vuHkZhtkwJ5X","outputId":"b014e3ad-838f-4c88-9956-295cf6b568d2"},"outputs":[{"data":{"text/plain":["LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n","              importance_type='split', learning_rate=0.01, max_bin=1200,\n","              max_depth=-1, min_child_samples=20, min_child_weight=0.001,\n","              min_split_gain=0.0, n_estimators=1000, n_jobs=-1, num_leaves=750,\n","              objective=None, random_state=123, reg_alpha=0.0, reg_lambda=0.0,\n","              silent=True, subsample=1.0, subsample_for_bin=200000,\n","              subsample_freq=0)"]},"execution_count":21,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["#Fitting light gbm model on the train data\n","from lightgbm import LGBMRegressor\n","lgbm = LGBMRegressor(random_state = 123 ,  num_leaves = 750 , learning_rate = 0.01, max_bin = 1200 , n_estimators = 1000)\n","lgbm.fit(train_final , y)"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":128739,"status":"ok","timestamp":1627873098062,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"0dsChNJGwJ5X","outputId":"218db856-d560-4178-b71e-ebc9729a6951"},"outputs":[{"name":"stdout","output_type":"stream","text":["[02:56:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"]},{"data":{"text/plain":["XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n","             colsample_bynode=1, colsample_bytree=1, gamma=0,\n","             importance_type='gain', learning_rate=0.2, max_delta_step=0,\n","             max_depth=7, min_child_weight=1, missing=None, n_estimators=1500,\n","             n_jobs=1, nthread=None, objective='reg:linear', random_state=123,\n","             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n","             silent=None, subsample=1, verbosity=1)"]},"execution_count":22,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["#Fitting xgboost regressor model on the train data\n","from xgboost import XGBRFRegressor\n","xgb = XGBRegressor(random_state = 123 , max_depth = 7 , learning_rate = 0.2 , n_estimators = 1500)\n","xgb.fit(train_final , y)"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":91414,"status":"ok","timestamp":1627873414352,"user":{"displayName":"Ranjitha Sridharan","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gj3P94R-HTC9j3qBEdS9MsUxxRCo9ylz7nAbhDs=s64","userId":"12057796790218647125"},"user_tz":-330},"id":"Zh-A0PsJwJ5X","outputId":"a810b3af-3301-4339-a19a-bdc263c3f991"},"outputs":[{"data":{"text/plain":["RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n","                      max_depth=45, max_features='auto', max_leaf_nodes=None,\n","                      max_samples=None, min_impurity_decrease=0.0,\n","                      min_impurity_split=None, min_samples_leaf=1,\n","                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n","                      n_estimators=600, n_jobs=None, oob_score=False,\n","                      random_state=123, verbose=0, warm_start=False)"]},"execution_count":23,"metadata":{"tags":[]},"output_type":"execute_result"}],"source":["#Fitting random forest regressor model on the train data\n","from sklearn.ensemble import RandomForestRegressor\n","rf = RandomForestRegressor(random_state = 123 , max_depth = 45 , n_estimators = 600)\n","rf.fit(train_final , y)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"N193-0ZjwJ5Z"},"outputs":[{"name":"stdout","output_type":"stream","text":["Learning rate set to 0.066066\n","0:\tlearn: 1.5436341\ttotal: 1.94s\tremaining: 32m 14s\n","1:\tlearn: 1.4990806\ttotal: 3.58s\tremaining: 29m 48s\n","2:\tlearn: 1.4561219\ttotal: 5.24s\tremaining: 29m 1s\n","3:\tlearn: 1.4173067\ttotal: 6.92s\tremaining: 28m 44s\n","4:\tlearn: 1.3821112\ttotal: 8.55s\tremaining: 28m 22s\n","5:\tlearn: 1.3491819\ttotal: 10.2s\tremaining: 28m 8s\n","6:\tlearn: 1.3184880\ttotal: 11.8s\tremaining: 27m 57s\n","7:\tlearn: 1.2879364\ttotal: 13.5s\tremaining: 27m 49s\n","8:\tlearn: 1.2625607\ttotal: 15.1s\tremaining: 27m 41s\n","9:\tlearn: 1.2397270\ttotal: 16.7s\tremaining: 27m 35s\n","10:\tlearn: 1.2182291\ttotal: 18.4s\tremaining: 27m 31s\n","11:\tlearn: 1.1979322\ttotal: 20s\tremaining: 27m 27s\n","12:\tlearn: 1.1774269\ttotal: 21.7s\tremaining: 27m 23s\n","13:\tlearn: 1.1610312\ttotal: 23.3s\tremaining: 27m 21s\n","14:\tlearn: 1.1448515\ttotal: 25s\tremaining: 27m 18s\n","15:\tlearn: 1.1266757\ttotal: 26.6s\tremaining: 27m 15s\n","16:\tlearn: 1.1105187\ttotal: 28.2s\tremaining: 27m 13s\n","17:\tlearn: 1.0976657\ttotal: 29.9s\tremaining: 27m 10s\n","18:\tlearn: 1.0830383\ttotal: 31.6s\tremaining: 27m 9s\n","19:\tlearn: 1.0682107\ttotal: 33.2s\tremaining: 27m 6s\n","20:\tlearn: 1.0562782\ttotal: 34.8s\tremaining: 27m 4s\n","21:\tlearn: 1.0455057\ttotal: 36.5s\tremaining: 27m 3s\n","22:\tlearn: 1.0327986\ttotal: 38.2s\tremaining: 27m\n","23:\tlearn: 1.0216183\ttotal: 39.8s\tremaining: 26m 58s\n","24:\tlearn: 1.0092100\ttotal: 41.5s\tremaining: 26m 56s\n","25:\tlearn: 0.9999393\ttotal: 43.1s\tremaining: 26m 53s\n","26:\tlearn: 0.9913449\ttotal: 44.7s\tremaining: 26m 51s\n","27:\tlearn: 0.9826689\ttotal: 46.4s\tremaining: 26m 49s\n","28:\tlearn: 0.9750124\ttotal: 48s\tremaining: 26m 48s\n","29:\tlearn: 0.9680465\ttotal: 49.7s\tremaining: 26m 46s\n","30:\tlearn: 0.9591146\ttotal: 51.3s\tremaining: 26m 44s\n","31:\tlearn: 0.9520316\ttotal: 53s\tremaining: 26m 42s\n","32:\tlearn: 0.9418150\ttotal: 54.6s\tremaining: 26m 41s\n","33:\tlearn: 0.9344902\ttotal: 56.3s\tremaining: 26m 39s\n","34:\tlearn: 0.9283586\ttotal: 57.9s\tremaining: 26m 37s\n","35:\tlearn: 0.9230881\ttotal: 59.6s\tremaining: 26m 35s\n","36:\tlearn: 0.9177359\ttotal: 1m 1s\tremaining: 26m 34s\n","37:\tlearn: 0.9119425\ttotal: 1m 2s\tremaining: 26m 32s\n","38:\tlearn: 0.9059997\ttotal: 1m 4s\tremaining: 26m 31s\n","39:\tlearn: 0.8997786\ttotal: 1m 6s\tremaining: 26m 29s\n","40:\tlearn: 0.8941010\ttotal: 1m 7s\tremaining: 26m 28s\n","41:\tlearn: 0.8888081\ttotal: 1m 9s\tremaining: 26m 26s\n","42:\tlearn: 0.8822953\ttotal: 1m 11s\tremaining: 26m 25s\n","43:\tlearn: 0.8785940\ttotal: 1m 12s\tremaining: 26m 23s\n","44:\tlearn: 0.8730263\ttotal: 1m 14s\tremaining: 26m 22s\n","45:\tlearn: 0.8676189\ttotal: 1m 16s\tremaining: 26m 20s\n","46:\tlearn: 0.8644868\ttotal: 1m 17s\tremaining: 26m 19s\n","47:\tlearn: 0.8600387\ttotal: 1m 19s\tremaining: 26m 17s\n","48:\tlearn: 0.8569161\ttotal: 1m 21s\tremaining: 26m 14s\n","49:\tlearn: 0.8525957\ttotal: 1m 22s\tremaining: 26m 12s\n","50:\tlearn: 0.8480478\ttotal: 1m 24s\tremaining: 26m 11s\n","51:\tlearn: 0.8441675\ttotal: 1m 26s\tremaining: 26m 9s\n","52:\tlearn: 0.8400685\ttotal: 1m 27s\tremaining: 26m 7s\n","53:\tlearn: 0.8354836\ttotal: 1m 29s\tremaining: 26m 5s\n","54:\tlearn: 0.8328932\ttotal: 1m 30s\tremaining: 26m 3s\n","55:\tlearn: 0.8281295\ttotal: 1m 32s\tremaining: 26m 1s\n","56:\tlearn: 0.8259945\ttotal: 1m 34s\tremaining: 25m 59s\n","57:\tlearn: 0.8216764\ttotal: 1m 35s\tremaining: 25m 57s\n","58:\tlearn: 0.8186456\ttotal: 1m 37s\tremaining: 25m 56s\n","59:\tlearn: 0.8146493\ttotal: 1m 39s\tremaining: 25m 54s\n","60:\tlearn: 0.8107563\ttotal: 1m 40s\tremaining: 25m 52s\n","61:\tlearn: 0.8078159\ttotal: 1m 42s\tremaining: 25m 50s\n","62:\tlearn: 0.8030310\ttotal: 1m 44s\tremaining: 25m 49s\n","63:\tlearn: 0.8004223\ttotal: 1m 45s\tremaining: 25m 47s\n","64:\tlearn: 0.7972044\ttotal: 1m 47s\tremaining: 25m 45s\n","65:\tlearn: 0.7931345\ttotal: 1m 49s\tremaining: 25m 43s\n","66:\tlearn: 0.7890967\ttotal: 1m 50s\tremaining: 25m 41s\n","67:\tlearn: 0.7844198\ttotal: 1m 52s\tremaining: 25m 40s\n","68:\tlearn: 0.7822399\ttotal: 1m 54s\tremaining: 25m 38s\n","69:\tlearn: 0.7791724\ttotal: 1m 55s\tremaining: 25m 36s\n","70:\tlearn: 0.7748214\ttotal: 1m 57s\tremaining: 25m 34s\n","71:\tlearn: 0.7713133\ttotal: 1m 58s\tremaining: 25m 32s\n","72:\tlearn: 0.7651391\ttotal: 2m\tremaining: 25m 31s\n","73:\tlearn: 0.7633451\ttotal: 2m 2s\tremaining: 25m 29s\n","74:\tlearn: 0.7608622\ttotal: 2m 3s\tremaining: 25m 27s\n","75:\tlearn: 0.7550680\ttotal: 2m 5s\tremaining: 25m 25s\n","76:\tlearn: 0.7523849\ttotal: 2m 7s\tremaining: 25m 24s\n","77:\tlearn: 0.7484563\ttotal: 2m 8s\tremaining: 25m 22s\n","78:\tlearn: 0.7455946\ttotal: 2m 10s\tremaining: 25m 20s\n","79:\tlearn: 0.7430261\ttotal: 2m 12s\tremaining: 25m 18s\n","80:\tlearn: 0.7388324\ttotal: 2m 13s\tremaining: 25m 17s\n","81:\tlearn: 0.7361620\ttotal: 2m 15s\tremaining: 25m 15s\n","82:\tlearn: 0.7324651\ttotal: 2m 17s\tremaining: 25m 13s\n","83:\tlearn: 0.7289511\ttotal: 2m 18s\tremaining: 25m 12s\n","84:\tlearn: 0.7271422\ttotal: 2m 20s\tremaining: 25m 10s\n","85:\tlearn: 0.7236179\ttotal: 2m 21s\tremaining: 25m 8s\n","86:\tlearn: 0.7202943\ttotal: 2m 23s\tremaining: 25m 7s\n","87:\tlearn: 0.7169609\ttotal: 2m 25s\tremaining: 25m 5s\n","88:\tlearn: 0.7148962\ttotal: 2m 26s\tremaining: 25m 3s\n","89:\tlearn: 0.7116274\ttotal: 2m 28s\tremaining: 25m 1s\n","90:\tlearn: 0.7098356\ttotal: 2m 30s\tremaining: 25m\n","91:\tlearn: 0.7081055\ttotal: 2m 31s\tremaining: 24m 58s\n","92:\tlearn: 0.7066846\ttotal: 2m 33s\tremaining: 24m 56s\n","93:\tlearn: 0.7038995\ttotal: 2m 35s\tremaining: 24m 54s\n","94:\tlearn: 0.7019539\ttotal: 2m 36s\tremaining: 24m 52s\n","95:\tlearn: 0.7002100\ttotal: 2m 38s\tremaining: 24m 50s\n","96:\tlearn: 0.6958564\ttotal: 2m 39s\tremaining: 24m 49s\n","97:\tlearn: 0.6939416\ttotal: 2m 41s\tremaining: 24m 47s\n","98:\tlearn: 0.6911982\ttotal: 2m 43s\tremaining: 24m 45s\n","99:\tlearn: 0.6887950\ttotal: 2m 44s\tremaining: 24m 44s\n","100:\tlearn: 0.6857736\ttotal: 2m 46s\tremaining: 24m 42s\n","101:\tlearn: 0.6838527\ttotal: 2m 48s\tremaining: 24m 40s\n","102:\tlearn: 0.6810155\ttotal: 2m 49s\tremaining: 24m 38s\n","103:\tlearn: 0.6796080\ttotal: 2m 51s\tremaining: 24m 36s\n","104:\tlearn: 0.6782466\ttotal: 2m 53s\tremaining: 24m 35s\n","105:\tlearn: 0.6761638\ttotal: 2m 54s\tremaining: 24m 33s\n","106:\tlearn: 0.6751478\ttotal: 2m 56s\tremaining: 24m 31s\n","107:\tlearn: 0.6741445\ttotal: 2m 57s\tremaining: 24m 29s\n","108:\tlearn: 0.6717315\ttotal: 2m 59s\tremaining: 24m 28s\n","109:\tlearn: 0.6684432\ttotal: 3m 1s\tremaining: 24m 26s\n","110:\tlearn: 0.6669549\ttotal: 3m 2s\tremaining: 24m 24s\n","111:\tlearn: 0.6646508\ttotal: 3m 4s\tremaining: 24m 23s\n","112:\tlearn: 0.6624482\ttotal: 3m 6s\tremaining: 24m 21s\n","113:\tlearn: 0.6611562\ttotal: 3m 7s\tremaining: 24m 19s\n","114:\tlearn: 0.6592211\ttotal: 3m 9s\tremaining: 24m 17s\n","115:\tlearn: 0.6567743\ttotal: 3m 11s\tremaining: 24m 16s\n","116:\tlearn: 0.6552148\ttotal: 3m 12s\tremaining: 24m 14s\n","117:\tlearn: 0.6532184\ttotal: 3m 14s\tremaining: 24m 13s\n","118:\tlearn: 0.6504699\ttotal: 3m 16s\tremaining: 24m 11s\n","119:\tlearn: 0.6488946\ttotal: 3m 17s\tremaining: 24m 9s\n","120:\tlearn: 0.6476564\ttotal: 3m 19s\tremaining: 24m 7s\n","121:\tlearn: 0.6460975\ttotal: 3m 20s\tremaining: 24m 6s\n","122:\tlearn: 0.6442103\ttotal: 3m 22s\tremaining: 24m 4s\n","123:\tlearn: 0.6429281\ttotal: 3m 24s\tremaining: 24m 2s\n","124:\tlearn: 0.6409474\ttotal: 3m 25s\tremaining: 24m 1s\n","125:\tlearn: 0.6387391\ttotal: 3m 27s\tremaining: 23m 59s\n","126:\tlearn: 0.6364699\ttotal: 3m 29s\tremaining: 23m 57s\n","127:\tlearn: 0.6354135\ttotal: 3m 30s\tremaining: 23m 55s\n","128:\tlearn: 0.6342399\ttotal: 3m 32s\tremaining: 23m 54s\n","129:\tlearn: 0.6311006\ttotal: 3m 34s\tremaining: 23m 52s\n","130:\tlearn: 0.6288718\ttotal: 3m 35s\tremaining: 23m 50s\n","131:\tlearn: 0.6279000\ttotal: 3m 37s\tremaining: 23m 49s\n","132:\tlearn: 0.6264056\ttotal: 3m 38s\tremaining: 23m 47s\n","133:\tlearn: 0.6242917\ttotal: 3m 40s\tremaining: 23m 45s\n","134:\tlearn: 0.6229684\ttotal: 3m 42s\tremaining: 23m 44s\n","135:\tlearn: 0.6210161\ttotal: 3m 43s\tremaining: 23m 42s\n","136:\tlearn: 0.6184013\ttotal: 3m 45s\tremaining: 23m 40s\n","137:\tlearn: 0.6168579\ttotal: 3m 47s\tremaining: 23m 39s\n","138:\tlearn: 0.6144384\ttotal: 3m 48s\tremaining: 23m 37s\n","139:\tlearn: 0.6130385\ttotal: 3m 50s\tremaining: 23m 35s\n","140:\tlearn: 0.6115629\ttotal: 3m 52s\tremaining: 23m 34s\n","141:\tlearn: 0.6095280\ttotal: 3m 53s\tremaining: 23m 32s\n","142:\tlearn: 0.6086325\ttotal: 3m 55s\tremaining: 23m 30s\n","143:\tlearn: 0.6067265\ttotal: 3m 57s\tremaining: 23m 28s\n","144:\tlearn: 0.6048110\ttotal: 3m 58s\tremaining: 23m 27s\n","145:\tlearn: 0.6033767\ttotal: 4m\tremaining: 23m 25s\n","146:\tlearn: 0.6020659\ttotal: 4m 1s\tremaining: 23m 23s\n","147:\tlearn: 0.5992921\ttotal: 4m 3s\tremaining: 23m 22s\n","148:\tlearn: 0.5979525\ttotal: 4m 5s\tremaining: 23m 20s\n","149:\tlearn: 0.5955600\ttotal: 4m 6s\tremaining: 23m 18s\n","150:\tlearn: 0.5944067\ttotal: 4m 8s\tremaining: 23m 17s\n","151:\tlearn: 0.5924996\ttotal: 4m 10s\tremaining: 23m 15s\n","152:\tlearn: 0.5904848\ttotal: 4m 11s\tremaining: 23m 13s\n","153:\tlearn: 0.5894712\ttotal: 4m 13s\tremaining: 23m 11s\n","154:\tlearn: 0.5882712\ttotal: 4m 15s\tremaining: 23m 10s\n","155:\tlearn: 0.5867938\ttotal: 4m 16s\tremaining: 23m 8s\n","156:\tlearn: 0.5845158\ttotal: 4m 18s\tremaining: 23m 6s\n","157:\tlearn: 0.5814452\ttotal: 4m 19s\tremaining: 23m 5s\n","158:\tlearn: 0.5794113\ttotal: 4m 21s\tremaining: 23m 3s\n","159:\tlearn: 0.5781410\ttotal: 4m 23s\tremaining: 23m 2s\n","160:\tlearn: 0.5763451\ttotal: 4m 24s\tremaining: 23m\n","161:\tlearn: 0.5749386\ttotal: 4m 26s\tremaining: 22m 58s\n","162:\tlearn: 0.5735318\ttotal: 4m 28s\tremaining: 22m 57s\n","163:\tlearn: 0.5715175\ttotal: 4m 29s\tremaining: 22m 55s\n","164:\tlearn: 0.5699217\ttotal: 4m 31s\tremaining: 22m 53s\n","165:\tlearn: 0.5675287\ttotal: 4m 33s\tremaining: 22m 51s\n","166:\tlearn: 0.5663185\ttotal: 4m 34s\tremaining: 22m 50s\n","167:\tlearn: 0.5651748\ttotal: 4m 36s\tremaining: 22m 48s\n","168:\tlearn: 0.5637687\ttotal: 4m 38s\tremaining: 22m 46s\n","169:\tlearn: 0.5627993\ttotal: 4m 39s\tremaining: 22m 45s\n","170:\tlearn: 0.5618125\ttotal: 4m 41s\tremaining: 22m 43s\n","171:\tlearn: 0.5605746\ttotal: 4m 42s\tremaining: 22m 42s\n","172:\tlearn: 0.5600906\ttotal: 4m 44s\tremaining: 22m 40s\n","173:\tlearn: 0.5584503\ttotal: 4m 46s\tremaining: 22m 38s\n","174:\tlearn: 0.5568957\ttotal: 4m 47s\tremaining: 22m 37s\n","175:\tlearn: 0.5553374\ttotal: 4m 49s\tremaining: 22m 35s\n","176:\tlearn: 0.5542346\ttotal: 4m 51s\tremaining: 22m 33s\n","177:\tlearn: 0.5523331\ttotal: 4m 52s\tremaining: 22m 32s\n","178:\tlearn: 0.5506804\ttotal: 4m 54s\tremaining: 22m 30s\n","179:\tlearn: 0.5486324\ttotal: 4m 56s\tremaining: 22m 28s\n","180:\tlearn: 0.5466661\ttotal: 4m 57s\tremaining: 22m 27s\n","181:\tlearn: 0.5450299\ttotal: 4m 59s\tremaining: 22m 25s\n","182:\tlearn: 0.5434363\ttotal: 5m 1s\tremaining: 22m 23s\n","183:\tlearn: 0.5419748\ttotal: 5m 2s\tremaining: 22m 22s\n","184:\tlearn: 0.5413277\ttotal: 5m 4s\tremaining: 22m 20s\n","185:\tlearn: 0.5393484\ttotal: 5m 5s\tremaining: 22m 18s\n","186:\tlearn: 0.5372610\ttotal: 5m 7s\tremaining: 22m 17s\n","187:\tlearn: 0.5356288\ttotal: 5m 9s\tremaining: 22m 15s\n","188:\tlearn: 0.5342864\ttotal: 5m 10s\tremaining: 22m 14s\n","189:\tlearn: 0.5334059\ttotal: 5m 12s\tremaining: 22m 12s\n","190:\tlearn: 0.5317332\ttotal: 5m 14s\tremaining: 22m 10s\n","191:\tlearn: 0.5299697\ttotal: 5m 15s\tremaining: 22m 9s\n","192:\tlearn: 0.5287984\ttotal: 5m 17s\tremaining: 22m 7s\n","193:\tlearn: 0.5268498\ttotal: 5m 19s\tremaining: 22m 5s\n","194:\tlearn: 0.5257509\ttotal: 5m 20s\tremaining: 22m 3s\n","195:\tlearn: 0.5249532\ttotal: 5m 22s\tremaining: 22m 2s\n","196:\tlearn: 0.5242462\ttotal: 5m 23s\tremaining: 22m\n","197:\tlearn: 0.5229845\ttotal: 5m 25s\tremaining: 21m 58s\n","198:\tlearn: 0.5216993\ttotal: 5m 27s\tremaining: 21m 57s\n","199:\tlearn: 0.5202330\ttotal: 5m 28s\tremaining: 21m 55s\n","200:\tlearn: 0.5182875\ttotal: 5m 30s\tremaining: 21m 53s\n","201:\tlearn: 0.5166884\ttotal: 5m 32s\tremaining: 21m 52s\n","202:\tlearn: 0.5150812\ttotal: 5m 33s\tremaining: 21m 50s\n","203:\tlearn: 0.5131324\ttotal: 5m 35s\tremaining: 21m 49s\n","204:\tlearn: 0.5111165\ttotal: 5m 37s\tremaining: 21m 47s\n","205:\tlearn: 0.5101115\ttotal: 5m 38s\tremaining: 21m 45s\n","206:\tlearn: 0.5093225\ttotal: 5m 40s\tremaining: 21m 44s\n","207:\tlearn: 0.5081405\ttotal: 5m 42s\tremaining: 21m 42s\n","208:\tlearn: 0.5067423\ttotal: 5m 43s\tremaining: 21m 41s\n","209:\tlearn: 0.5061252\ttotal: 5m 45s\tremaining: 21m 39s\n","210:\tlearn: 0.5041128\ttotal: 5m 47s\tremaining: 21m 37s\n","211:\tlearn: 0.5029698\ttotal: 5m 48s\tremaining: 21m 36s\n","212:\tlearn: 0.5021143\ttotal: 5m 50s\tremaining: 21m 34s\n","213:\tlearn: 0.5005268\ttotal: 5m 52s\tremaining: 21m 33s\n","214:\tlearn: 0.4992169\ttotal: 5m 53s\tremaining: 21m 31s\n","215:\tlearn: 0.4983221\ttotal: 5m 55s\tremaining: 21m 29s\n","216:\tlearn: 0.4973467\ttotal: 5m 56s\tremaining: 21m 28s\n","217:\tlearn: 0.4963549\ttotal: 5m 58s\tremaining: 21m 26s\n","218:\tlearn: 0.4946347\ttotal: 6m\tremaining: 21m 24s\n","219:\tlearn: 0.4928681\ttotal: 6m 1s\tremaining: 21m 23s\n","220:\tlearn: 0.4919539\ttotal: 6m 3s\tremaining: 21m 21s\n","221:\tlearn: 0.4905148\ttotal: 6m 5s\tremaining: 21m 19s\n","222:\tlearn: 0.4895519\ttotal: 6m 6s\tremaining: 21m 18s\n","223:\tlearn: 0.4884233\ttotal: 6m 8s\tremaining: 21m 16s\n","224:\tlearn: 0.4875467\ttotal: 6m 10s\tremaining: 21m 15s\n","225:\tlearn: 0.4860318\ttotal: 6m 11s\tremaining: 21m 13s\n","226:\tlearn: 0.4852073\ttotal: 6m 13s\tremaining: 21m 11s\n","227:\tlearn: 0.4841477\ttotal: 6m 15s\tremaining: 21m 10s\n","228:\tlearn: 0.4831251\ttotal: 6m 16s\tremaining: 21m 8s\n","229:\tlearn: 0.4818797\ttotal: 6m 18s\tremaining: 21m 7s\n","230:\tlearn: 0.4802758\ttotal: 6m 20s\tremaining: 21m 5s\n","231:\tlearn: 0.4792178\ttotal: 6m 21s\tremaining: 21m 3s\n","232:\tlearn: 0.4778956\ttotal: 6m 23s\tremaining: 21m 2s\n","233:\tlearn: 0.4767792\ttotal: 6m 25s\tremaining: 21m\n","234:\tlearn: 0.4758434\ttotal: 6m 26s\tremaining: 20m 58s\n","235:\tlearn: 0.4740190\ttotal: 6m 28s\tremaining: 20m 57s\n","236:\tlearn: 0.4729270\ttotal: 6m 29s\tremaining: 20m 55s\n","237:\tlearn: 0.4716031\ttotal: 6m 31s\tremaining: 20m 53s\n","238:\tlearn: 0.4705103\ttotal: 6m 33s\tremaining: 20m 52s\n","239:\tlearn: 0.4689636\ttotal: 6m 34s\tremaining: 20m 50s\n","240:\tlearn: 0.4674346\ttotal: 6m 36s\tremaining: 20m 49s\n","241:\tlearn: 0.4667839\ttotal: 6m 38s\tremaining: 20m 47s\n","242:\tlearn: 0.4657040\ttotal: 6m 39s\tremaining: 20m 45s\n","243:\tlearn: 0.4650735\ttotal: 6m 41s\tremaining: 20m 44s\n","244:\tlearn: 0.4644229\ttotal: 6m 43s\tremaining: 20m 42s\n","245:\tlearn: 0.4632819\ttotal: 6m 44s\tremaining: 20m 40s\n","246:\tlearn: 0.4622676\ttotal: 6m 46s\tremaining: 20m 39s\n","247:\tlearn: 0.4611960\ttotal: 6m 48s\tremaining: 20m 37s\n","248:\tlearn: 0.4604330\ttotal: 6m 49s\tremaining: 20m 35s\n","249:\tlearn: 0.4587437\ttotal: 6m 51s\tremaining: 20m 34s\n","250:\tlearn: 0.4574239\ttotal: 6m 53s\tremaining: 20m 32s\n","251:\tlearn: 0.4563835\ttotal: 6m 54s\tremaining: 20m 30s\n","252:\tlearn: 0.4555413\ttotal: 6m 56s\tremaining: 20m 29s\n","253:\tlearn: 0.4541202\ttotal: 6m 57s\tremaining: 20m 27s\n","254:\tlearn: 0.4530189\ttotal: 6m 59s\tremaining: 20m 25s\n","255:\tlearn: 0.4527656\ttotal: 7m 1s\tremaining: 20m 24s\n","256:\tlearn: 0.4517067\ttotal: 7m 2s\tremaining: 20m 22s\n","257:\tlearn: 0.4510964\ttotal: 7m 4s\tremaining: 20m 21s\n","258:\tlearn: 0.4502729\ttotal: 7m 6s\tremaining: 20m 19s\n","259:\tlearn: 0.4494302\ttotal: 7m 7s\tremaining: 20m 17s\n","260:\tlearn: 0.4489246\ttotal: 7m 9s\tremaining: 20m 16s\n","261:\tlearn: 0.4482944\ttotal: 7m 11s\tremaining: 20m 14s\n","262:\tlearn: 0.4475513\ttotal: 7m 12s\tremaining: 20m 12s\n","263:\tlearn: 0.4464686\ttotal: 7m 14s\tremaining: 20m 11s\n","264:\tlearn: 0.4453567\ttotal: 7m 16s\tremaining: 20m 9s\n","265:\tlearn: 0.4442746\ttotal: 7m 17s\tremaining: 20m 8s\n","266:\tlearn: 0.4437121\ttotal: 7m 19s\tremaining: 20m 6s\n","267:\tlearn: 0.4429469\ttotal: 7m 21s\tremaining: 20m 4s\n","268:\tlearn: 0.4420098\ttotal: 7m 22s\tremaining: 20m 3s\n","269:\tlearn: 0.4409754\ttotal: 7m 24s\tremaining: 20m 1s\n","270:\tlearn: 0.4401652\ttotal: 7m 26s\tremaining: 19m 59s\n","271:\tlearn: 0.4393058\ttotal: 7m 27s\tremaining: 19m 58s\n","272:\tlearn: 0.4384589\ttotal: 7m 29s\tremaining: 19m 56s\n","273:\tlearn: 0.4368317\ttotal: 7m 31s\tremaining: 19m 55s\n","274:\tlearn: 0.4358018\ttotal: 7m 32s\tremaining: 19m 53s\n","275:\tlearn: 0.4349984\ttotal: 7m 34s\tremaining: 19m 51s\n","276:\tlearn: 0.4342906\ttotal: 7m 35s\tremaining: 19m 50s\n","277:\tlearn: 0.4334354\ttotal: 7m 37s\tremaining: 19m 48s\n","278:\tlearn: 0.4327405\ttotal: 7m 39s\tremaining: 19m 46s\n","279:\tlearn: 0.4320596\ttotal: 7m 40s\tremaining: 19m 45s\n","280:\tlearn: 0.4313491\ttotal: 7m 42s\tremaining: 19m 43s\n","281:\tlearn: 0.4301567\ttotal: 7m 44s\tremaining: 19m 42s\n","282:\tlearn: 0.4294071\ttotal: 7m 45s\tremaining: 19m 40s\n","283:\tlearn: 0.4283267\ttotal: 7m 47s\tremaining: 19m 38s\n","284:\tlearn: 0.4273007\ttotal: 7m 49s\tremaining: 19m 37s\n","285:\tlearn: 0.4259635\ttotal: 7m 50s\tremaining: 19m 35s\n","286:\tlearn: 0.4252445\ttotal: 7m 52s\tremaining: 19m 33s\n","287:\tlearn: 0.4245143\ttotal: 7m 54s\tremaining: 19m 32s\n","288:\tlearn: 0.4234294\ttotal: 7m 55s\tremaining: 19m 30s\n","289:\tlearn: 0.4228489\ttotal: 7m 57s\tremaining: 19m 29s\n","290:\tlearn: 0.4221969\ttotal: 7m 59s\tremaining: 19m 27s\n","291:\tlearn: 0.4216986\ttotal: 8m\tremaining: 19m 25s\n","292:\tlearn: 0.4206476\ttotal: 8m 2s\tremaining: 19m 24s\n","293:\tlearn: 0.4197055\ttotal: 8m 4s\tremaining: 19m 22s\n","294:\tlearn: 0.4190030\ttotal: 8m 5s\tremaining: 19m 20s\n","295:\tlearn: 0.4181642\ttotal: 8m 7s\tremaining: 19m 19s\n","296:\tlearn: 0.4174567\ttotal: 8m 9s\tremaining: 19m 17s\n","297:\tlearn: 0.4164714\ttotal: 8m 10s\tremaining: 19m 15s\n","298:\tlearn: 0.4151243\ttotal: 8m 12s\tremaining: 19m 14s\n","299:\tlearn: 0.4139862\ttotal: 8m 14s\tremaining: 19m 12s\n","300:\tlearn: 0.4129968\ttotal: 8m 15s\tremaining: 19m 11s\n","301:\tlearn: 0.4122782\ttotal: 8m 17s\tremaining: 19m 9s\n","302:\tlearn: 0.4118059\ttotal: 8m 18s\tremaining: 19m 7s\n","303:\tlearn: 0.4115005\ttotal: 8m 20s\tremaining: 19m 6s\n","304:\tlearn: 0.4104825\ttotal: 8m 22s\tremaining: 19m 4s\n","305:\tlearn: 0.4098706\ttotal: 8m 23s\tremaining: 19m 2s\n","306:\tlearn: 0.4087907\ttotal: 8m 25s\tremaining: 19m 1s\n","307:\tlearn: 0.4078228\ttotal: 8m 27s\tremaining: 18m 59s\n","308:\tlearn: 0.4069618\ttotal: 8m 28s\tremaining: 18m 57s\n","309:\tlearn: 0.4064406\ttotal: 8m 30s\tremaining: 18m 56s\n","310:\tlearn: 0.4059562\ttotal: 8m 32s\tremaining: 18m 54s\n","311:\tlearn: 0.4056851\ttotal: 8m 33s\tremaining: 18m 53s\n","312:\tlearn: 0.4050977\ttotal: 8m 35s\tremaining: 18m 51s\n","313:\tlearn: 0.4044378\ttotal: 8m 37s\tremaining: 18m 49s\n","314:\tlearn: 0.4037462\ttotal: 8m 38s\tremaining: 18m 48s\n","315:\tlearn: 0.4033245\ttotal: 8m 40s\tremaining: 18m 46s\n","316:\tlearn: 0.4025002\ttotal: 8m 42s\tremaining: 18m 44s\n","317:\tlearn: 0.4017961\ttotal: 8m 43s\tremaining: 18m 43s\n","318:\tlearn: 0.4010759\ttotal: 8m 45s\tremaining: 18m 41s\n","319:\tlearn: 0.3999491\ttotal: 8m 46s\tremaining: 18m 39s\n","320:\tlearn: 0.3993277\ttotal: 8m 48s\tremaining: 18m 38s\n","321:\tlearn: 0.3987574\ttotal: 8m 50s\tremaining: 18m 36s\n","322:\tlearn: 0.3983179\ttotal: 8m 51s\tremaining: 18m 34s\n","323:\tlearn: 0.3975526\ttotal: 8m 53s\tremaining: 18m 33s\n","324:\tlearn: 0.3966466\ttotal: 8m 55s\tremaining: 18m 31s\n","325:\tlearn: 0.3957587\ttotal: 8m 56s\tremaining: 18m 29s\n","326:\tlearn: 0.3949166\ttotal: 8m 58s\tremaining: 18m 28s\n","327:\tlearn: 0.3939141\ttotal: 9m\tremaining: 18m 26s\n","328:\tlearn: 0.3928768\ttotal: 9m 1s\tremaining: 18m 24s\n","329:\tlearn: 0.3921384\ttotal: 9m 3s\tremaining: 18m 23s\n","330:\tlearn: 0.3916831\ttotal: 9m 5s\tremaining: 18m 21s\n","331:\tlearn: 0.3906775\ttotal: 9m 6s\tremaining: 18m 19s\n","332:\tlearn: 0.3902879\ttotal: 9m 8s\tremaining: 18m 18s\n","333:\tlearn: 0.3891933\ttotal: 9m 9s\tremaining: 18m 16s\n","334:\tlearn: 0.3882448\ttotal: 9m 11s\tremaining: 18m 14s\n","335:\tlearn: 0.3875656\ttotal: 9m 13s\tremaining: 18m 13s\n","336:\tlearn: 0.3868371\ttotal: 9m 14s\tremaining: 18m 11s\n","337:\tlearn: 0.3861984\ttotal: 9m 16s\tremaining: 18m 10s\n","338:\tlearn: 0.3857402\ttotal: 9m 18s\tremaining: 18m 8s\n","339:\tlearn: 0.3854211\ttotal: 9m 19s\tremaining: 18m 6s\n","340:\tlearn: 0.3846621\ttotal: 9m 21s\tremaining: 18m 5s\n","341:\tlearn: 0.3834028\ttotal: 9m 23s\tremaining: 18m 3s\n","342:\tlearn: 0.3828575\ttotal: 9m 24s\tremaining: 18m 1s\n","343:\tlearn: 0.3824636\ttotal: 9m 26s\tremaining: 18m\n","344:\tlearn: 0.3819815\ttotal: 9m 28s\tremaining: 17m 58s\n","345:\tlearn: 0.3811200\ttotal: 9m 29s\tremaining: 17m 56s\n","346:\tlearn: 0.3804686\ttotal: 9m 31s\tremaining: 17m 55s\n","347:\tlearn: 0.3798274\ttotal: 9m 33s\tremaining: 17m 53s\n","348:\tlearn: 0.3788980\ttotal: 9m 34s\tremaining: 17m 51s\n","349:\tlearn: 0.3782184\ttotal: 9m 36s\tremaining: 17m 50s\n","350:\tlearn: 0.3773254\ttotal: 9m 37s\tremaining: 17m 48s\n","351:\tlearn: 0.3768996\ttotal: 9m 39s\tremaining: 17m 47s\n","352:\tlearn: 0.3762324\ttotal: 9m 41s\tremaining: 17m 45s\n","353:\tlearn: 0.3757514\ttotal: 9m 42s\tremaining: 17m 43s\n","354:\tlearn: 0.3751762\ttotal: 9m 44s\tremaining: 17m 42s\n","355:\tlearn: 0.3751646\ttotal: 9m 44s\tremaining: 17m 37s\n","356:\tlearn: 0.3740138\ttotal: 9m 46s\tremaining: 17m 35s\n","357:\tlearn: 0.3732123\ttotal: 9m 47s\tremaining: 17m 34s\n","358:\tlearn: 0.3726677\ttotal: 9m 49s\tremaining: 17m 32s\n","359:\tlearn: 0.3718558\ttotal: 9m 51s\tremaining: 17m 30s\n","360:\tlearn: 0.3710908\ttotal: 9m 52s\tremaining: 17m 29s\n","361:\tlearn: 0.3702590\ttotal: 9m 54s\tremaining: 17m 27s\n","362:\tlearn: 0.3695299\ttotal: 9m 56s\tremaining: 17m 25s\n","363:\tlearn: 0.3686230\ttotal: 9m 57s\tremaining: 17m 24s\n","364:\tlearn: 0.3680373\ttotal: 9m 59s\tremaining: 17m 22s\n","365:\tlearn: 0.3672095\ttotal: 10m\tremaining: 17m 21s\n","366:\tlearn: 0.3667681\ttotal: 10m 2s\tremaining: 17m 19s\n","367:\tlearn: 0.3662218\ttotal: 10m 4s\tremaining: 17m 17s\n","368:\tlearn: 0.3657278\ttotal: 10m 5s\tremaining: 17m 16s\n","369:\tlearn: 0.3651534\ttotal: 10m 7s\tremaining: 17m 14s\n","370:\tlearn: 0.3646309\ttotal: 10m 9s\tremaining: 17m 12s\n","371:\tlearn: 0.3638475\ttotal: 10m 10s\tremaining: 17m 11s\n","372:\tlearn: 0.3630305\ttotal: 10m 12s\tremaining: 17m 9s\n","373:\tlearn: 0.3625915\ttotal: 10m 14s\tremaining: 17m 7s\n","374:\tlearn: 0.3618052\ttotal: 10m 15s\tremaining: 17m 6s\n","375:\tlearn: 0.3615580\ttotal: 10m 17s\tremaining: 17m 4s\n","376:\tlearn: 0.3609949\ttotal: 10m 18s\tremaining: 17m 2s\n","377:\tlearn: 0.3603947\ttotal: 10m 20s\tremaining: 17m 1s\n","378:\tlearn: 0.3598027\ttotal: 10m 22s\tremaining: 16m 59s\n","379:\tlearn: 0.3592613\ttotal: 10m 23s\tremaining: 16m 57s\n","380:\tlearn: 0.3587236\ttotal: 10m 25s\tremaining: 16m 56s\n","381:\tlearn: 0.3579597\ttotal: 10m 27s\tremaining: 16m 54s\n","382:\tlearn: 0.3573266\ttotal: 10m 28s\tremaining: 16m 52s\n","383:\tlearn: 0.3567379\ttotal: 10m 30s\tremaining: 16m 51s\n","384:\tlearn: 0.3564980\ttotal: 10m 32s\tremaining: 16m 49s\n","385:\tlearn: 0.3555290\ttotal: 10m 33s\tremaining: 16m 48s\n","386:\tlearn: 0.3546652\ttotal: 10m 35s\tremaining: 16m 46s\n","387:\tlearn: 0.3545467\ttotal: 10m 37s\tremaining: 16m 44s\n","388:\tlearn: 0.3539716\ttotal: 10m 38s\tremaining: 16m 43s\n","389:\tlearn: 0.3536341\ttotal: 10m 40s\tremaining: 16m 41s\n","390:\tlearn: 0.3526677\ttotal: 10m 41s\tremaining: 16m 39s\n","391:\tlearn: 0.3521297\ttotal: 10m 43s\tremaining: 16m 38s\n","392:\tlearn: 0.3516816\ttotal: 10m 45s\tremaining: 16m 36s\n","393:\tlearn: 0.3506243\ttotal: 10m 46s\tremaining: 16m 34s\n","394:\tlearn: 0.3499857\ttotal: 10m 48s\tremaining: 16m 33s\n","395:\tlearn: 0.3495142\ttotal: 10m 50s\tremaining: 16m 31s\n","396:\tlearn: 0.3494092\ttotal: 10m 51s\tremaining: 16m 29s\n","397:\tlearn: 0.3490521\ttotal: 10m 53s\tremaining: 16m 28s\n","398:\tlearn: 0.3485959\ttotal: 10m 54s\tremaining: 16m 26s\n","399:\tlearn: 0.3479480\ttotal: 10m 56s\tremaining: 16m 24s\n","400:\tlearn: 0.3472707\ttotal: 10m 58s\tremaining: 16m 23s\n","401:\tlearn: 0.3462511\ttotal: 10m 59s\tremaining: 16m 21s\n","402:\tlearn: 0.3457094\ttotal: 11m 1s\tremaining: 16m 20s\n","403:\tlearn: 0.3451527\ttotal: 11m 3s\tremaining: 16m 18s\n","404:\tlearn: 0.3445566\ttotal: 11m 4s\tremaining: 16m 16s\n","405:\tlearn: 0.3442900\ttotal: 11m 6s\tremaining: 16m 15s\n","406:\tlearn: 0.3439610\ttotal: 11m 8s\tremaining: 16m 13s\n","407:\tlearn: 0.3435799\ttotal: 11m 9s\tremaining: 16m 11s\n","408:\tlearn: 0.3428668\ttotal: 11m 11s\tremaining: 16m 10s\n","409:\tlearn: 0.3425279\ttotal: 11m 13s\tremaining: 16m 8s\n","410:\tlearn: 0.3420672\ttotal: 11m 14s\tremaining: 16m 6s\n","411:\tlearn: 0.3416357\ttotal: 11m 16s\tremaining: 16m 5s\n","412:\tlearn: 0.3407189\ttotal: 11m 18s\tremaining: 16m 3s\n","413:\tlearn: 0.3401206\ttotal: 11m 19s\tremaining: 16m 2s\n","414:\tlearn: 0.3392443\ttotal: 11m 21s\tremaining: 16m\n","415:\tlearn: 0.3385322\ttotal: 11m 22s\tremaining: 15m 58s\n","416:\tlearn: 0.3380024\ttotal: 11m 24s\tremaining: 15m 57s\n","417:\tlearn: 0.3373395\ttotal: 11m 26s\tremaining: 15m 55s\n","418:\tlearn: 0.3368655\ttotal: 11m 27s\tremaining: 15m 53s\n","419:\tlearn: 0.3367217\ttotal: 11m 29s\tremaining: 15m 52s\n","420:\tlearn: 0.3360761\ttotal: 11m 31s\tremaining: 15m 50s\n","421:\tlearn: 0.3356634\ttotal: 11m 32s\tremaining: 15m 48s\n","422:\tlearn: 0.3351621\ttotal: 11m 34s\tremaining: 15m 47s\n","423:\tlearn: 0.3348367\ttotal: 11m 36s\tremaining: 15m 45s\n","424:\tlearn: 0.3342167\ttotal: 11m 37s\tremaining: 15m 44s\n","425:\tlearn: 0.3337916\ttotal: 11m 39s\tremaining: 15m 42s\n","426:\tlearn: 0.3334990\ttotal: 11m 41s\tremaining: 15m 40s\n","427:\tlearn: 0.3326697\ttotal: 11m 42s\tremaining: 15m 39s\n","428:\tlearn: 0.3321633\ttotal: 11m 44s\tremaining: 15m 37s\n","429:\tlearn: 0.3315567\ttotal: 11m 46s\tremaining: 15m 35s\n","430:\tlearn: 0.3308872\ttotal: 11m 47s\tremaining: 15m 34s\n","431:\tlearn: 0.3303614\ttotal: 11m 49s\tremaining: 15m 32s\n","432:\tlearn: 0.3299831\ttotal: 11m 50s\tremaining: 15m 31s\n","433:\tlearn: 0.3294679\ttotal: 11m 52s\tremaining: 15m 29s\n","434:\tlearn: 0.3288631\ttotal: 11m 54s\tremaining: 15m 27s\n","435:\tlearn: 0.3284769\ttotal: 11m 55s\tremaining: 15m 26s\n","436:\tlearn: 0.3277888\ttotal: 11m 57s\tremaining: 15m 24s\n","437:\tlearn: 0.3266836\ttotal: 11m 59s\tremaining: 15m 22s\n","438:\tlearn: 0.3264403\ttotal: 12m\tremaining: 15m 21s\n","439:\tlearn: 0.3256956\ttotal: 12m 2s\tremaining: 15m 19s\n","440:\tlearn: 0.3249494\ttotal: 12m 4s\tremaining: 15m 17s\n","441:\tlearn: 0.3246074\ttotal: 12m 5s\tremaining: 15m 16s\n","442:\tlearn: 0.3242598\ttotal: 12m 7s\tremaining: 15m 14s\n","443:\tlearn: 0.3238582\ttotal: 12m 9s\tremaining: 15m 12s\n","444:\tlearn: 0.3232138\ttotal: 12m 10s\tremaining: 15m 11s\n","445:\tlearn: 0.3230379\ttotal: 12m 12s\tremaining: 15m 9s\n","446:\tlearn: 0.3222981\ttotal: 12m 14s\tremaining: 15m 8s\n","447:\tlearn: 0.3216540\ttotal: 12m 15s\tremaining: 15m 6s\n","448:\tlearn: 0.3212294\ttotal: 12m 17s\tremaining: 15m 4s\n","449:\tlearn: 0.3205370\ttotal: 12m 18s\tremaining: 15m 3s\n","450:\tlearn: 0.3200649\ttotal: 12m 20s\tremaining: 15m 1s\n","451:\tlearn: 0.3199649\ttotal: 12m 22s\tremaining: 14m 59s\n","452:\tlearn: 0.3191821\ttotal: 12m 23s\tremaining: 14m 58s\n","453:\tlearn: 0.3186052\ttotal: 12m 25s\tremaining: 14m 56s\n","454:\tlearn: 0.3182218\ttotal: 12m 27s\tremaining: 14m 55s\n","455:\tlearn: 0.3176992\ttotal: 12m 28s\tremaining: 14m 53s\n","456:\tlearn: 0.3171710\ttotal: 12m 30s\tremaining: 14m 51s\n","457:\tlearn: 0.3169938\ttotal: 12m 32s\tremaining: 14m 50s\n","458:\tlearn: 0.3165804\ttotal: 12m 33s\tremaining: 14m 48s\n","459:\tlearn: 0.3161335\ttotal: 12m 35s\tremaining: 14m 46s\n","460:\tlearn: 0.3156480\ttotal: 12m 37s\tremaining: 14m 45s\n","461:\tlearn: 0.3154547\ttotal: 12m 38s\tremaining: 14m 43s\n","462:\tlearn: 0.3149752\ttotal: 12m 40s\tremaining: 14m 41s\n","463:\tlearn: 0.3145120\ttotal: 12m 41s\tremaining: 14m 40s\n","464:\tlearn: 0.3139227\ttotal: 12m 43s\tremaining: 14m 38s\n","465:\tlearn: 0.3135610\ttotal: 12m 45s\tremaining: 14m 36s\n","466:\tlearn: 0.3132671\ttotal: 12m 46s\tremaining: 14m 35s\n","467:\tlearn: 0.3126322\ttotal: 12m 48s\tremaining: 14m 33s\n","468:\tlearn: 0.3123638\ttotal: 12m 50s\tremaining: 14m 32s\n","469:\tlearn: 0.3122761\ttotal: 12m 51s\tremaining: 14m 30s\n","470:\tlearn: 0.3114296\ttotal: 12m 53s\tremaining: 14m 28s\n","471:\tlearn: 0.3110141\ttotal: 12m 55s\tremaining: 14m 27s\n","472:\tlearn: 0.3107176\ttotal: 12m 56s\tremaining: 14m 25s\n","473:\tlearn: 0.3104392\ttotal: 12m 58s\tremaining: 14m 23s\n","474:\tlearn: 0.3099688\ttotal: 13m\tremaining: 14m 22s\n","475:\tlearn: 0.3092567\ttotal: 13m 1s\tremaining: 14m 20s\n","476:\tlearn: 0.3092014\ttotal: 13m 3s\tremaining: 14m 18s\n","477:\tlearn: 0.3088703\ttotal: 13m 4s\tremaining: 14m 17s\n","478:\tlearn: 0.3081697\ttotal: 13m 6s\tremaining: 14m 15s\n","479:\tlearn: 0.3079868\ttotal: 13m 8s\tremaining: 14m 13s\n","480:\tlearn: 0.3076330\ttotal: 13m 9s\tremaining: 14m 12s\n","481:\tlearn: 0.3074233\ttotal: 13m 11s\tremaining: 14m 10s\n","482:\tlearn: 0.3068101\ttotal: 13m 13s\tremaining: 14m 8s\n","483:\tlearn: 0.3064607\ttotal: 13m 14s\tremaining: 14m 7s\n","484:\tlearn: 0.3061577\ttotal: 13m 16s\tremaining: 14m 5s\n","485:\tlearn: 0.3057285\ttotal: 13m 18s\tremaining: 14m 4s\n","486:\tlearn: 0.3053368\ttotal: 13m 19s\tremaining: 14m 2s\n","487:\tlearn: 0.3048687\ttotal: 13m 21s\tremaining: 14m\n","488:\tlearn: 0.3042436\ttotal: 13m 22s\tremaining: 13m 59s\n","489:\tlearn: 0.3037887\ttotal: 13m 24s\tremaining: 13m 57s\n","490:\tlearn: 0.3034468\ttotal: 13m 26s\tremaining: 13m 55s\n","491:\tlearn: 0.3031135\ttotal: 13m 27s\tremaining: 13m 54s\n","492:\tlearn: 0.3021185\ttotal: 13m 29s\tremaining: 13m 52s\n","493:\tlearn: 0.3017980\ttotal: 13m 31s\tremaining: 13m 50s\n","494:\tlearn: 0.3014307\ttotal: 13m 32s\tremaining: 13m 49s\n","495:\tlearn: 0.3010011\ttotal: 13m 34s\tremaining: 13m 47s\n","496:\tlearn: 0.3003709\ttotal: 13m 36s\tremaining: 13m 45s\n","497:\tlearn: 0.2999855\ttotal: 13m 37s\tremaining: 13m 44s\n","498:\tlearn: 0.2995830\ttotal: 13m 39s\tremaining: 13m 42s\n","499:\tlearn: 0.2995667\ttotal: 13m 41s\tremaining: 13m 41s\n","500:\tlearn: 0.2991721\ttotal: 13m 42s\tremaining: 13m 39s\n","501:\tlearn: 0.2984785\ttotal: 13m 44s\tremaining: 13m 37s\n","502:\tlearn: 0.2981713\ttotal: 13m 45s\tremaining: 13m 36s\n","503:\tlearn: 0.2977391\ttotal: 13m 47s\tremaining: 13m 34s\n","504:\tlearn: 0.2975593\ttotal: 13m 49s\tremaining: 13m 32s\n","505:\tlearn: 0.2969603\ttotal: 13m 50s\tremaining: 13m 31s\n","506:\tlearn: 0.2966589\ttotal: 13m 52s\tremaining: 13m 29s\n","507:\tlearn: 0.2964253\ttotal: 13m 54s\tremaining: 13m 27s\n","508:\tlearn: 0.2960733\ttotal: 13m 55s\tremaining: 13m 26s\n","509:\tlearn: 0.2957181\ttotal: 13m 57s\tremaining: 13m 24s\n","510:\tlearn: 0.2953601\ttotal: 13m 59s\tremaining: 13m 22s\n","511:\tlearn: 0.2950522\ttotal: 14m\tremaining: 13m 21s\n","512:\tlearn: 0.2948790\ttotal: 14m 2s\tremaining: 13m 19s\n","513:\tlearn: 0.2944769\ttotal: 14m 3s\tremaining: 13m 17s\n","514:\tlearn: 0.2941721\ttotal: 14m 5s\tremaining: 13m 16s\n","515:\tlearn: 0.2937338\ttotal: 14m 7s\tremaining: 13m 14s\n","516:\tlearn: 0.2935034\ttotal: 14m 8s\tremaining: 13m 13s\n","517:\tlearn: 0.2929227\ttotal: 14m 10s\tremaining: 13m 11s\n","518:\tlearn: 0.2924805\ttotal: 14m 12s\tremaining: 13m 9s\n","519:\tlearn: 0.2921805\ttotal: 14m 13s\tremaining: 13m 8s\n","520:\tlearn: 0.2916980\ttotal: 14m 15s\tremaining: 13m 6s\n","521:\tlearn: 0.2913676\ttotal: 14m 17s\tremaining: 13m 4s\n","522:\tlearn: 0.2911693\ttotal: 14m 18s\tremaining: 13m 3s\n","523:\tlearn: 0.2907596\ttotal: 14m 20s\tremaining: 13m 1s\n","524:\tlearn: 0.2904733\ttotal: 14m 21s\tremaining: 12m 59s\n","525:\tlearn: 0.2900631\ttotal: 14m 23s\tremaining: 12m 58s\n","526:\tlearn: 0.2894681\ttotal: 14m 25s\tremaining: 12m 56s\n","527:\tlearn: 0.2889436\ttotal: 14m 26s\tremaining: 12m 54s\n","528:\tlearn: 0.2887026\ttotal: 14m 28s\tremaining: 12m 53s\n","529:\tlearn: 0.2883647\ttotal: 14m 30s\tremaining: 12m 51s\n","530:\tlearn: 0.2875869\ttotal: 14m 31s\tremaining: 12m 50s\n","531:\tlearn: 0.2873874\ttotal: 14m 33s\tremaining: 12m 48s\n","532:\tlearn: 0.2869768\ttotal: 14m 35s\tremaining: 12m 46s\n","533:\tlearn: 0.2865008\ttotal: 14m 36s\tremaining: 12m 45s\n","534:\tlearn: 0.2860561\ttotal: 14m 38s\tremaining: 12m 43s\n","535:\tlearn: 0.2855002\ttotal: 14m 40s\tremaining: 12m 41s\n","536:\tlearn: 0.2850707\ttotal: 14m 41s\tremaining: 12m 40s\n","537:\tlearn: 0.2842506\ttotal: 14m 43s\tremaining: 12m 38s\n","538:\tlearn: 0.2837974\ttotal: 14m 44s\tremaining: 12m 36s\n","539:\tlearn: 0.2831984\ttotal: 14m 46s\tremaining: 12m 35s\n","540:\tlearn: 0.2826606\ttotal: 14m 48s\tremaining: 12m 33s\n","541:\tlearn: 0.2823251\ttotal: 14m 49s\tremaining: 12m 31s\n","542:\tlearn: 0.2821053\ttotal: 14m 51s\tremaining: 12m 30s\n","543:\tlearn: 0.2819049\ttotal: 14m 53s\tremaining: 12m 28s\n","544:\tlearn: 0.2815055\ttotal: 14m 54s\tremaining: 12m 26s\n","545:\tlearn: 0.2811144\ttotal: 14m 56s\tremaining: 12m 25s\n","546:\tlearn: 0.2802631\ttotal: 14m 58s\tremaining: 12m 23s\n","547:\tlearn: 0.2794922\ttotal: 14m 59s\tremaining: 12m 22s\n","548:\tlearn: 0.2792419\ttotal: 15m 1s\tremaining: 12m 20s\n","549:\tlearn: 0.2786273\ttotal: 15m 2s\tremaining: 12m 18s\n","550:\tlearn: 0.2784036\ttotal: 15m 4s\tremaining: 12m 17s\n","551:\tlearn: 0.2778700\ttotal: 15m 6s\tremaining: 12m 15s\n","552:\tlearn: 0.2775916\ttotal: 15m 7s\tremaining: 12m 13s\n","553:\tlearn: 0.2771791\ttotal: 15m 9s\tremaining: 12m 12s\n","554:\tlearn: 0.2768833\ttotal: 15m 11s\tremaining: 12m 10s\n","555:\tlearn: 0.2763552\ttotal: 15m 12s\tremaining: 12m 8s\n","556:\tlearn: 0.2760074\ttotal: 15m 14s\tremaining: 12m 7s\n","557:\tlearn: 0.2759209\ttotal: 15m 16s\tremaining: 12m 5s\n","558:\tlearn: 0.2755898\ttotal: 15m 17s\tremaining: 12m 3s\n","559:\tlearn: 0.2753631\ttotal: 15m 19s\tremaining: 12m 2s\n","560:\tlearn: 0.2752436\ttotal: 15m 20s\tremaining: 12m\n","561:\tlearn: 0.2747737\ttotal: 15m 22s\tremaining: 11m 59s\n","562:\tlearn: 0.2742148\ttotal: 15m 24s\tremaining: 11m 57s\n","563:\tlearn: 0.2738923\ttotal: 15m 25s\tremaining: 11m 55s\n","564:\tlearn: 0.2732709\ttotal: 15m 27s\tremaining: 11m 54s\n","565:\tlearn: 0.2730501\ttotal: 15m 29s\tremaining: 11m 52s\n","566:\tlearn: 0.2727173\ttotal: 15m 30s\tremaining: 11m 50s\n","567:\tlearn: 0.2726894\ttotal: 15m 32s\tremaining: 11m 49s\n","568:\tlearn: 0.2722349\ttotal: 15m 34s\tremaining: 11m 47s\n","569:\tlearn: 0.2718328\ttotal: 15m 35s\tremaining: 11m 45s\n","570:\tlearn: 0.2716612\ttotal: 15m 37s\tremaining: 11m 44s\n","571:\tlearn: 0.2714974\ttotal: 15m 38s\tremaining: 11m 42s\n","572:\tlearn: 0.2709692\ttotal: 15m 40s\tremaining: 11m 40s\n","573:\tlearn: 0.2705754\ttotal: 15m 42s\tremaining: 11m 39s\n","574:\tlearn: 0.2701227\ttotal: 15m 43s\tremaining: 11m 37s\n","575:\tlearn: 0.2696810\ttotal: 15m 45s\tremaining: 11m 36s\n","576:\tlearn: 0.2694256\ttotal: 15m 47s\tremaining: 11m 34s\n","577:\tlearn: 0.2690274\ttotal: 15m 48s\tremaining: 11m 32s\n","578:\tlearn: 0.2686864\ttotal: 15m 50s\tremaining: 11m 31s\n","579:\tlearn: 0.2683303\ttotal: 15m 52s\tremaining: 11m 29s\n","580:\tlearn: 0.2680719\ttotal: 15m 53s\tremaining: 11m 27s\n","581:\tlearn: 0.2675910\ttotal: 15m 55s\tremaining: 11m 26s\n","582:\tlearn: 0.2671543\ttotal: 15m 57s\tremaining: 11m 24s\n","583:\tlearn: 0.2665960\ttotal: 15m 58s\tremaining: 11m 22s\n","584:\tlearn: 0.2660758\ttotal: 16m\tremaining: 11m 21s\n","585:\tlearn: 0.2657071\ttotal: 16m 1s\tremaining: 11m 19s\n","586:\tlearn: 0.2655289\ttotal: 16m 3s\tremaining: 11m 17s\n","587:\tlearn: 0.2651861\ttotal: 16m 5s\tremaining: 11m 16s\n","588:\tlearn: 0.2648101\ttotal: 16m 6s\tremaining: 11m 14s\n","589:\tlearn: 0.2646220\ttotal: 16m 8s\tremaining: 11m 13s\n","590:\tlearn: 0.2643993\ttotal: 16m 10s\tremaining: 11m 11s\n","591:\tlearn: 0.2640483\ttotal: 16m 11s\tremaining: 11m 9s\n","592:\tlearn: 0.2636876\ttotal: 16m 13s\tremaining: 11m 8s\n","593:\tlearn: 0.2636401\ttotal: 16m 15s\tremaining: 11m 6s\n","594:\tlearn: 0.2634082\ttotal: 16m 16s\tremaining: 11m 4s\n","595:\tlearn: 0.2631848\ttotal: 16m 18s\tremaining: 11m 3s\n","596:\tlearn: 0.2628914\ttotal: 16m 19s\tremaining: 11m 1s\n","597:\tlearn: 0.2620143\ttotal: 16m 21s\tremaining: 10m 59s\n","598:\tlearn: 0.2615098\ttotal: 16m 23s\tremaining: 10m 58s\n","599:\tlearn: 0.2611934\ttotal: 16m 24s\tremaining: 10m 56s\n","600:\tlearn: 0.2609072\ttotal: 16m 26s\tremaining: 10m 54s\n","601:\tlearn: 0.2603697\ttotal: 16m 28s\tremaining: 10m 53s\n","602:\tlearn: 0.2600768\ttotal: 16m 29s\tremaining: 10m 51s\n","603:\tlearn: 0.2597161\ttotal: 16m 31s\tremaining: 10m 49s\n","604:\tlearn: 0.2592729\ttotal: 16m 33s\tremaining: 10m 48s\n","605:\tlearn: 0.2591234\ttotal: 16m 34s\tremaining: 10m 46s\n","606:\tlearn: 0.2587027\ttotal: 16m 36s\tremaining: 10m 45s\n","607:\tlearn: 0.2581914\ttotal: 16m 37s\tremaining: 10m 43s\n","608:\tlearn: 0.2579582\ttotal: 16m 39s\tremaining: 10m 41s\n","609:\tlearn: 0.2578648\ttotal: 16m 41s\tremaining: 10m 40s\n","610:\tlearn: 0.2576142\ttotal: 16m 42s\tremaining: 10m 38s\n","611:\tlearn: 0.2571393\ttotal: 16m 44s\tremaining: 10m 36s\n","612:\tlearn: 0.2569391\ttotal: 16m 46s\tremaining: 10m 35s\n","613:\tlearn: 0.2566753\ttotal: 16m 47s\tremaining: 10m 33s\n","614:\tlearn: 0.2565010\ttotal: 16m 49s\tremaining: 10m 31s\n","615:\tlearn: 0.2562196\ttotal: 16m 51s\tremaining: 10m 30s\n","616:\tlearn: 0.2558885\ttotal: 16m 52s\tremaining: 10m 28s\n","617:\tlearn: 0.2556680\ttotal: 16m 54s\tremaining: 10m 26s\n","618:\tlearn: 0.2554276\ttotal: 16m 55s\tremaining: 10m 25s\n","619:\tlearn: 0.2550689\ttotal: 16m 57s\tremaining: 10m 23s\n","620:\tlearn: 0.2547949\ttotal: 16m 59s\tremaining: 10m 22s\n","621:\tlearn: 0.2544029\ttotal: 17m\tremaining: 10m 20s\n","622:\tlearn: 0.2539912\ttotal: 17m 2s\tremaining: 10m 18s\n","623:\tlearn: 0.2538467\ttotal: 17m 4s\tremaining: 10m 17s\n","624:\tlearn: 0.2534533\ttotal: 17m 5s\tremaining: 10m 15s\n","625:\tlearn: 0.2531565\ttotal: 17m 7s\tremaining: 10m 13s\n","626:\tlearn: 0.2529693\ttotal: 17m 8s\tremaining: 10m 12s\n","627:\tlearn: 0.2526052\ttotal: 17m 10s\tremaining: 10m 10s\n","628:\tlearn: 0.2523172\ttotal: 17m 12s\tremaining: 10m 8s\n","629:\tlearn: 0.2519151\ttotal: 17m 13s\tremaining: 10m 7s\n","630:\tlearn: 0.2517312\ttotal: 17m 15s\tremaining: 10m 5s\n","631:\tlearn: 0.2514315\ttotal: 17m 17s\tremaining: 10m 3s\n","632:\tlearn: 0.2510804\ttotal: 17m 18s\tremaining: 10m 2s\n","633:\tlearn: 0.2508702\ttotal: 17m 20s\tremaining: 10m\n","634:\tlearn: 0.2505779\ttotal: 17m 22s\tremaining: 9m 58s\n","635:\tlearn: 0.2504878\ttotal: 17m 23s\tremaining: 9m 57s\n","636:\tlearn: 0.2501088\ttotal: 17m 25s\tremaining: 9m 55s\n","637:\tlearn: 0.2498714\ttotal: 17m 26s\tremaining: 9m 54s\n","638:\tlearn: 0.2495856\ttotal: 17m 28s\tremaining: 9m 52s\n","639:\tlearn: 0.2491673\ttotal: 17m 30s\tremaining: 9m 50s\n","640:\tlearn: 0.2487570\ttotal: 17m 31s\tremaining: 9m 49s\n","641:\tlearn: 0.2485254\ttotal: 17m 33s\tremaining: 9m 47s\n","642:\tlearn: 0.2482450\ttotal: 17m 35s\tremaining: 9m 45s\n","643:\tlearn: 0.2479378\ttotal: 17m 36s\tremaining: 9m 44s\n","644:\tlearn: 0.2479101\ttotal: 17m 38s\tremaining: 9m 42s\n","645:\tlearn: 0.2476591\ttotal: 17m 40s\tremaining: 9m 40s\n","646:\tlearn: 0.2473375\ttotal: 17m 41s\tremaining: 9m 39s\n","647:\tlearn: 0.2472887\ttotal: 17m 43s\tremaining: 9m 37s\n","648:\tlearn: 0.2472089\ttotal: 17m 44s\tremaining: 9m 35s\n","649:\tlearn: 0.2468525\ttotal: 17m 46s\tremaining: 9m 34s\n","650:\tlearn: 0.2465471\ttotal: 17m 48s\tremaining: 9m 32s\n","651:\tlearn: 0.2462380\ttotal: 17m 49s\tremaining: 9m 31s\n","652:\tlearn: 0.2459166\ttotal: 17m 51s\tremaining: 9m 29s\n","653:\tlearn: 0.2455496\ttotal: 17m 53s\tremaining: 9m 27s\n","654:\tlearn: 0.2450489\ttotal: 17m 54s\tremaining: 9m 26s\n","655:\tlearn: 0.2450109\ttotal: 17m 56s\tremaining: 9m 24s\n","656:\tlearn: 0.2443272\ttotal: 17m 58s\tremaining: 9m 22s\n","657:\tlearn: 0.2440990\ttotal: 17m 59s\tremaining: 9m 21s\n","658:\tlearn: 0.2439176\ttotal: 18m 1s\tremaining: 9m 19s\n","659:\tlearn: 0.2433930\ttotal: 18m 2s\tremaining: 9m 17s\n","660:\tlearn: 0.2428924\ttotal: 18m 4s\tremaining: 9m 16s\n","661:\tlearn: 0.2426989\ttotal: 18m 6s\tremaining: 9m 14s\n","662:\tlearn: 0.2426483\ttotal: 18m 7s\tremaining: 9m 12s\n","663:\tlearn: 0.2423974\ttotal: 18m 9s\tremaining: 9m 11s\n","664:\tlearn: 0.2421750\ttotal: 18m 11s\tremaining: 9m 9s\n","665:\tlearn: 0.2419559\ttotal: 18m 12s\tremaining: 9m 8s\n","666:\tlearn: 0.2415671\ttotal: 18m 14s\tremaining: 9m 6s\n","667:\tlearn: 0.2413256\ttotal: 18m 16s\tremaining: 9m 4s\n","668:\tlearn: 0.2411760\ttotal: 18m 17s\tremaining: 9m 3s\n","669:\tlearn: 0.2409527\ttotal: 18m 19s\tremaining: 9m 1s\n","670:\tlearn: 0.2407488\ttotal: 18m 20s\tremaining: 8m 59s\n","671:\tlearn: 0.2405236\ttotal: 18m 22s\tremaining: 8m 58s\n","672:\tlearn: 0.2403629\ttotal: 18m 24s\tremaining: 8m 56s\n","673:\tlearn: 0.2399791\ttotal: 18m 25s\tremaining: 8m 54s\n","674:\tlearn: 0.2398955\ttotal: 18m 27s\tremaining: 8m 53s\n","675:\tlearn: 0.2397598\ttotal: 18m 29s\tremaining: 8m 51s\n","676:\tlearn: 0.2396741\ttotal: 18m 30s\tremaining: 8m 49s\n","677:\tlearn: 0.2394657\ttotal: 18m 32s\tremaining: 8m 48s\n","678:\tlearn: 0.2391060\ttotal: 18m 33s\tremaining: 8m 46s\n","679:\tlearn: 0.2389263\ttotal: 18m 35s\tremaining: 8m 44s\n","680:\tlearn: 0.2385861\ttotal: 18m 37s\tremaining: 8m 43s\n","681:\tlearn: 0.2384429\ttotal: 18m 38s\tremaining: 8m 41s\n","682:\tlearn: 0.2380003\ttotal: 18m 40s\tremaining: 8m 40s\n","683:\tlearn: 0.2378424\ttotal: 18m 42s\tremaining: 8m 38s\n","684:\tlearn: 0.2375877\ttotal: 18m 43s\tremaining: 8m 36s\n","685:\tlearn: 0.2374135\ttotal: 18m 45s\tremaining: 8m 35s\n","686:\tlearn: 0.2371160\ttotal: 18m 47s\tremaining: 8m 33s\n","687:\tlearn: 0.2366868\ttotal: 18m 48s\tremaining: 8m 31s\n","688:\tlearn: 0.2366389\ttotal: 18m 50s\tremaining: 8m 30s\n","689:\tlearn: 0.2365169\ttotal: 18m 52s\tremaining: 8m 28s\n","690:\tlearn: 0.2362823\ttotal: 18m 53s\tremaining: 8m 26s\n","691:\tlearn: 0.2360427\ttotal: 18m 55s\tremaining: 8m 25s\n","692:\tlearn: 0.2354683\ttotal: 18m 56s\tremaining: 8m 23s\n","693:\tlearn: 0.2353452\ttotal: 18m 58s\tremaining: 8m 22s\n","694:\tlearn: 0.2350082\ttotal: 19m\tremaining: 8m 20s\n","695:\tlearn: 0.2346911\ttotal: 19m 1s\tremaining: 8m 18s\n","696:\tlearn: 0.2345587\ttotal: 19m 3s\tremaining: 8m 17s\n","697:\tlearn: 0.2341307\ttotal: 19m 5s\tremaining: 8m 15s\n","698:\tlearn: 0.2339353\ttotal: 19m 6s\tremaining: 8m 13s\n","699:\tlearn: 0.2338958\ttotal: 19m 8s\tremaining: 8m 12s\n","700:\tlearn: 0.2336113\ttotal: 19m 10s\tremaining: 8m 10s\n","701:\tlearn: 0.2333169\ttotal: 19m 11s\tremaining: 8m 8s\n","702:\tlearn: 0.2331269\ttotal: 19m 13s\tremaining: 8m 7s\n","703:\tlearn: 0.2329947\ttotal: 19m 14s\tremaining: 8m 5s\n","704:\tlearn: 0.2324800\ttotal: 19m 16s\tremaining: 8m 3s\n","705:\tlearn: 0.2323534\ttotal: 19m 18s\tremaining: 8m 2s\n","706:\tlearn: 0.2319798\ttotal: 19m 19s\tremaining: 8m\n","707:\tlearn: 0.2315031\ttotal: 19m 21s\tremaining: 7m 59s\n","708:\tlearn: 0.2313473\ttotal: 19m 23s\tremaining: 7m 57s\n","709:\tlearn: 0.2310021\ttotal: 19m 24s\tremaining: 7m 55s\n","710:\tlearn: 0.2309099\ttotal: 19m 26s\tremaining: 7m 54s\n","711:\tlearn: 0.2305613\ttotal: 19m 28s\tremaining: 7m 52s\n","712:\tlearn: 0.2302001\ttotal: 19m 29s\tremaining: 7m 50s\n","713:\tlearn: 0.2298573\ttotal: 19m 31s\tremaining: 7m 49s\n","714:\tlearn: 0.2294473\ttotal: 19m 33s\tremaining: 7m 47s\n","715:\tlearn: 0.2292356\ttotal: 19m 34s\tremaining: 7m 45s\n","716:\tlearn: 0.2290269\ttotal: 19m 36s\tremaining: 7m 44s\n","717:\tlearn: 0.2289305\ttotal: 19m 38s\tremaining: 7m 42s\n","718:\tlearn: 0.2286294\ttotal: 19m 39s\tremaining: 7m 41s\n","719:\tlearn: 0.2283170\ttotal: 19m 41s\tremaining: 7m 39s\n","720:\tlearn: 0.2278462\ttotal: 19m 42s\tremaining: 7m 37s\n","721:\tlearn: 0.2275281\ttotal: 19m 44s\tremaining: 7m 36s\n","722:\tlearn: 0.2271638\ttotal: 19m 46s\tremaining: 7m 34s\n","723:\tlearn: 0.2269075\ttotal: 19m 47s\tremaining: 7m 32s\n","724:\tlearn: 0.2266897\ttotal: 19m 49s\tremaining: 7m 31s\n","725:\tlearn: 0.2263525\ttotal: 19m 51s\tremaining: 7m 29s\n","726:\tlearn: 0.2260638\ttotal: 19m 52s\tremaining: 7m 27s\n","727:\tlearn: 0.2258831\ttotal: 19m 54s\tremaining: 7m 26s\n","728:\tlearn: 0.2255974\ttotal: 19m 56s\tremaining: 7m 24s\n","729:\tlearn: 0.2254904\ttotal: 19m 57s\tremaining: 7m 23s\n","730:\tlearn: 0.2250582\ttotal: 19m 59s\tremaining: 7m 21s\n","731:\tlearn: 0.2248786\ttotal: 20m 1s\tremaining: 7m 19s\n","732:\tlearn: 0.2247739\ttotal: 20m 2s\tremaining: 7m 18s\n","733:\tlearn: 0.2245538\ttotal: 20m 4s\tremaining: 7m 16s\n","734:\tlearn: 0.2242878\ttotal: 20m 5s\tremaining: 7m 14s\n","735:\tlearn: 0.2240653\ttotal: 20m 7s\tremaining: 7m 13s\n","736:\tlearn: 0.2239266\ttotal: 20m 9s\tremaining: 7m 11s\n","737:\tlearn: 0.2236748\ttotal: 20m 10s\tremaining: 7m 9s\n","738:\tlearn: 0.2235580\ttotal: 20m 12s\tremaining: 7m 8s\n","739:\tlearn: 0.2234363\ttotal: 20m 14s\tremaining: 7m 6s\n","740:\tlearn: 0.2230686\ttotal: 20m 15s\tremaining: 7m 4s\n","741:\tlearn: 0.2227289\ttotal: 20m 17s\tremaining: 7m 3s\n","742:\tlearn: 0.2224905\ttotal: 20m 19s\tremaining: 7m 1s\n","743:\tlearn: 0.2219709\ttotal: 20m 20s\tremaining: 7m\n","744:\tlearn: 0.2218374\ttotal: 20m 22s\tremaining: 6m 58s\n","745:\tlearn: 0.2216061\ttotal: 20m 23s\tremaining: 6m 56s\n","746:\tlearn: 0.2212454\ttotal: 20m 25s\tremaining: 6m 55s\n","747:\tlearn: 0.2210053\ttotal: 20m 27s\tremaining: 6m 53s\n","748:\tlearn: 0.2205873\ttotal: 20m 28s\tremaining: 6m 51s\n","749:\tlearn: 0.2202696\ttotal: 20m 30s\tremaining: 6m 50s\n","750:\tlearn: 0.2197710\ttotal: 20m 32s\tremaining: 6m 48s\n","751:\tlearn: 0.2194220\ttotal: 20m 33s\tremaining: 6m 46s\n","752:\tlearn: 0.2191270\ttotal: 20m 35s\tremaining: 6m 45s\n","753:\tlearn: 0.2188902\ttotal: 20m 37s\tremaining: 6m 43s\n","754:\tlearn: 0.2185789\ttotal: 20m 38s\tremaining: 6m 41s\n","755:\tlearn: 0.2183083\ttotal: 20m 40s\tremaining: 6m 40s\n","756:\tlearn: 0.2181189\ttotal: 20m 42s\tremaining: 6m 38s\n","757:\tlearn: 0.2178511\ttotal: 20m 43s\tremaining: 6m 37s\n","758:\tlearn: 0.2176067\ttotal: 20m 45s\tremaining: 6m 35s\n","759:\tlearn: 0.2173596\ttotal: 20m 46s\tremaining: 6m 33s\n","760:\tlearn: 0.2171774\ttotal: 20m 48s\tremaining: 6m 32s\n","761:\tlearn: 0.2169135\ttotal: 20m 50s\tremaining: 6m 30s\n","762:\tlearn: 0.2166749\ttotal: 20m 51s\tremaining: 6m 28s\n","763:\tlearn: 0.2165285\ttotal: 20m 53s\tremaining: 6m 27s\n","764:\tlearn: 0.2162195\ttotal: 20m 55s\tremaining: 6m 25s\n","765:\tlearn: 0.2159399\ttotal: 20m 56s\tremaining: 6m 23s\n","766:\tlearn: 0.2158066\ttotal: 20m 58s\tremaining: 6m 22s\n","767:\tlearn: 0.2155413\ttotal: 20m 59s\tremaining: 6m 20s\n","768:\tlearn: 0.2151884\ttotal: 21m 1s\tremaining: 6m 18s\n","769:\tlearn: 0.2149490\ttotal: 21m 3s\tremaining: 6m 17s\n","770:\tlearn: 0.2147598\ttotal: 21m 4s\tremaining: 6m 15s\n","771:\tlearn: 0.2146760\ttotal: 21m 6s\tremaining: 6m 14s\n","772:\tlearn: 0.2144181\ttotal: 21m 8s\tremaining: 6m 12s\n","773:\tlearn: 0.2141825\ttotal: 21m 9s\tremaining: 6m 10s\n","774:\tlearn: 0.2140096\ttotal: 21m 11s\tremaining: 6m 9s\n","775:\tlearn: 0.2137737\ttotal: 21m 12s\tremaining: 6m 7s\n","776:\tlearn: 0.2135717\ttotal: 21m 14s\tremaining: 6m 5s\n","777:\tlearn: 0.2133751\ttotal: 21m 16s\tremaining: 6m 4s\n","778:\tlearn: 0.2130606\ttotal: 21m 17s\tremaining: 6m 2s\n","779:\tlearn: 0.2129989\ttotal: 21m 19s\tremaining: 6m\n","780:\tlearn: 0.2126478\ttotal: 21m 21s\tremaining: 5m 59s\n","781:\tlearn: 0.2124335\ttotal: 21m 22s\tremaining: 5m 57s\n","782:\tlearn: 0.2122191\ttotal: 21m 24s\tremaining: 5m 55s\n","783:\tlearn: 0.2120118\ttotal: 21m 26s\tremaining: 5m 54s\n","784:\tlearn: 0.2117985\ttotal: 21m 27s\tremaining: 5m 52s\n","785:\tlearn: 0.2115478\ttotal: 21m 29s\tremaining: 5m 51s\n","786:\tlearn: 0.2113298\ttotal: 21m 30s\tremaining: 5m 49s\n","787:\tlearn: 0.2109759\ttotal: 21m 32s\tremaining: 5m 47s\n","788:\tlearn: 0.2107581\ttotal: 21m 34s\tremaining: 5m 46s\n","789:\tlearn: 0.2105533\ttotal: 21m 35s\tremaining: 5m 44s\n","790:\tlearn: 0.2103384\ttotal: 21m 37s\tremaining: 5m 42s\n","791:\tlearn: 0.2102400\ttotal: 21m 39s\tremaining: 5m 41s\n","792:\tlearn: 0.2101188\ttotal: 21m 40s\tremaining: 5m 39s\n","793:\tlearn: 0.2100412\ttotal: 21m 42s\tremaining: 5m 37s\n","794:\tlearn: 0.2097923\ttotal: 21m 44s\tremaining: 5m 36s\n","795:\tlearn: 0.2096098\ttotal: 21m 45s\tremaining: 5m 34s\n","796:\tlearn: 0.2094705\ttotal: 21m 47s\tremaining: 5m 32s\n","797:\tlearn: 0.2094211\ttotal: 21m 47s\tremaining: 5m 31s\n","798:\tlearn: 0.2091696\ttotal: 21m 49s\tremaining: 5m 29s\n","799:\tlearn: 0.2090051\ttotal: 21m 50s\tremaining: 5m 27s\n","800:\tlearn: 0.2086122\ttotal: 21m 52s\tremaining: 5m 26s\n","801:\tlearn: 0.2084907\ttotal: 21m 54s\tremaining: 5m 24s\n","802:\tlearn: 0.2083042\ttotal: 21m 55s\tremaining: 5m 22s\n","803:\tlearn: 0.2079480\ttotal: 21m 57s\tremaining: 5m 21s\n","804:\tlearn: 0.2077303\ttotal: 21m 59s\tremaining: 5m 19s\n","805:\tlearn: 0.2076203\ttotal: 22m\tremaining: 5m 17s\n","806:\tlearn: 0.2075068\ttotal: 22m 2s\tremaining: 5m 16s\n","807:\tlearn: 0.2072847\ttotal: 22m 3s\tremaining: 5m 14s\n","808:\tlearn: 0.2069574\ttotal: 22m 5s\tremaining: 5m 12s\n","809:\tlearn: 0.2068022\ttotal: 22m 7s\tremaining: 5m 11s\n","810:\tlearn: 0.2065949\ttotal: 22m 8s\tremaining: 5m 9s\n","811:\tlearn: 0.2064567\ttotal: 22m 10s\tremaining: 5m 8s\n","812:\tlearn: 0.2062270\ttotal: 22m 12s\tremaining: 5m 6s\n","813:\tlearn: 0.2059912\ttotal: 22m 13s\tremaining: 5m 4s\n","814:\tlearn: 0.2057989\ttotal: 22m 15s\tremaining: 5m 3s\n","815:\tlearn: 0.2057759\ttotal: 22m 17s\tremaining: 5m 1s\n","816:\tlearn: 0.2055803\ttotal: 22m 18s\tremaining: 4m 59s\n","817:\tlearn: 0.2054271\ttotal: 22m 20s\tremaining: 4m 58s\n","818:\tlearn: 0.2051127\ttotal: 22m 21s\tremaining: 4m 56s\n","819:\tlearn: 0.2047976\ttotal: 22m 23s\tremaining: 4m 54s\n","820:\tlearn: 0.2045225\ttotal: 22m 25s\tremaining: 4m 53s\n","821:\tlearn: 0.2043230\ttotal: 22m 26s\tremaining: 4m 51s\n","822:\tlearn: 0.2041550\ttotal: 22m 28s\tremaining: 4m 49s\n","823:\tlearn: 0.2038846\ttotal: 22m 30s\tremaining: 4m 48s\n","824:\tlearn: 0.2037341\ttotal: 22m 31s\tremaining: 4m 46s\n","825:\tlearn: 0.2036172\ttotal: 22m 33s\tremaining: 4m 45s\n","826:\tlearn: 0.2034079\ttotal: 22m 34s\tremaining: 4m 43s\n","827:\tlearn: 0.2031340\ttotal: 22m 36s\tremaining: 4m 41s\n","828:\tlearn: 0.2029214\ttotal: 22m 38s\tremaining: 4m 40s\n","829:\tlearn: 0.2028564\ttotal: 22m 39s\tremaining: 4m 38s\n","830:\tlearn: 0.2026062\ttotal: 22m 41s\tremaining: 4m 36s\n","831:\tlearn: 0.2023851\ttotal: 22m 43s\tremaining: 4m 35s\n","832:\tlearn: 0.2022523\ttotal: 22m 44s\tremaining: 4m 33s\n","833:\tlearn: 0.2019452\ttotal: 22m 46s\tremaining: 4m 31s\n","834:\tlearn: 0.2016393\ttotal: 22m 47s\tremaining: 4m 30s\n","835:\tlearn: 0.2013538\ttotal: 22m 49s\tremaining: 4m 28s\n","836:\tlearn: 0.2011038\ttotal: 22m 51s\tremaining: 4m 27s\n","837:\tlearn: 0.2008920\ttotal: 22m 52s\tremaining: 4m 25s\n","838:\tlearn: 0.2007536\ttotal: 22m 54s\tremaining: 4m 23s\n","839:\tlearn: 0.2004655\ttotal: 22m 56s\tremaining: 4m 22s\n","840:\tlearn: 0.2001766\ttotal: 22m 57s\tremaining: 4m 20s\n","841:\tlearn: 0.1998997\ttotal: 22m 59s\tremaining: 4m 18s\n","842:\tlearn: 0.1997912\ttotal: 23m 1s\tremaining: 4m 17s\n","843:\tlearn: 0.1995848\ttotal: 23m 2s\tremaining: 4m 15s\n","844:\tlearn: 0.1993663\ttotal: 23m 4s\tremaining: 4m 13s\n","845:\tlearn: 0.1991702\ttotal: 23m 5s\tremaining: 4m 12s\n","846:\tlearn: 0.1989689\ttotal: 23m 7s\tremaining: 4m 10s\n","847:\tlearn: 0.1988314\ttotal: 23m 9s\tremaining: 4m 8s\n","848:\tlearn: 0.1986273\ttotal: 23m 10s\tremaining: 4m 7s\n","849:\tlearn: 0.1986126\ttotal: 23m 10s\tremaining: 4m 5s\n","850:\tlearn: 0.1985573\ttotal: 23m 12s\tremaining: 4m 3s\n","851:\tlearn: 0.1984659\ttotal: 23m 14s\tremaining: 4m 2s\n","852:\tlearn: 0.1982791\ttotal: 23m 15s\tremaining: 4m\n","853:\tlearn: 0.1980802\ttotal: 23m 17s\tremaining: 3m 58s\n","854:\tlearn: 0.1979359\ttotal: 23m 18s\tremaining: 3m 57s\n","855:\tlearn: 0.1976517\ttotal: 23m 20s\tremaining: 3m 55s\n","856:\tlearn: 0.1974179\ttotal: 23m 22s\tremaining: 3m 53s\n","857:\tlearn: 0.1971942\ttotal: 23m 23s\tremaining: 3m 52s\n","858:\tlearn: 0.1969689\ttotal: 23m 25s\tremaining: 3m 50s\n","859:\tlearn: 0.1968202\ttotal: 23m 27s\tremaining: 3m 49s\n","860:\tlearn: 0.1966272\ttotal: 23m 28s\tremaining: 3m 47s\n","861:\tlearn: 0.1963448\ttotal: 23m 30s\tremaining: 3m 45s\n","862:\tlearn: 0.1961483\ttotal: 23m 31s\tremaining: 3m 44s\n","863:\tlearn: 0.1958223\ttotal: 23m 33s\tremaining: 3m 42s\n","864:\tlearn: 0.1956634\ttotal: 23m 35s\tremaining: 3m 40s\n","865:\tlearn: 0.1952854\ttotal: 23m 36s\tremaining: 3m 39s\n","866:\tlearn: 0.1950457\ttotal: 23m 38s\tremaining: 3m 37s\n","867:\tlearn: 0.1948551\ttotal: 23m 40s\tremaining: 3m 35s\n","868:\tlearn: 0.1946727\ttotal: 23m 41s\tremaining: 3m 34s\n","869:\tlearn: 0.1945373\ttotal: 23m 43s\tremaining: 3m 32s\n","870:\tlearn: 0.1943512\ttotal: 23m 44s\tremaining: 3m 31s\n","871:\tlearn: 0.1942133\ttotal: 23m 46s\tremaining: 3m 29s\n","872:\tlearn: 0.1940071\ttotal: 23m 48s\tremaining: 3m 27s\n","873:\tlearn: 0.1938618\ttotal: 23m 49s\tremaining: 3m 26s\n","874:\tlearn: 0.1938081\ttotal: 23m 51s\tremaining: 3m 24s\n","875:\tlearn: 0.1936990\ttotal: 23m 53s\tremaining: 3m 22s\n","876:\tlearn: 0.1935665\ttotal: 23m 54s\tremaining: 3m 21s\n","877:\tlearn: 0.1932652\ttotal: 23m 56s\tremaining: 3m 19s\n","878:\tlearn: 0.1931551\ttotal: 23m 57s\tremaining: 3m 17s\n","879:\tlearn: 0.1929240\ttotal: 23m 59s\tremaining: 3m 16s\n","880:\tlearn: 0.1927990\ttotal: 24m 1s\tremaining: 3m 14s\n","881:\tlearn: 0.1925305\ttotal: 24m 2s\tremaining: 3m 13s\n","882:\tlearn: 0.1923650\ttotal: 24m 4s\tremaining: 3m 11s\n","883:\tlearn: 0.1921177\ttotal: 24m 6s\tremaining: 3m 9s\n","884:\tlearn: 0.1919195\ttotal: 24m 7s\tremaining: 3m 8s\n","885:\tlearn: 0.1918142\ttotal: 24m 9s\tremaining: 3m 6s\n","886:\tlearn: 0.1917326\ttotal: 24m 10s\tremaining: 3m 4s\n","887:\tlearn: 0.1915894\ttotal: 24m 12s\tremaining: 3m 3s\n","888:\tlearn: 0.1914065\ttotal: 24m 14s\tremaining: 3m 1s\n","889:\tlearn: 0.1912381\ttotal: 24m 15s\tremaining: 2m 59s\n","890:\tlearn: 0.1910785\ttotal: 24m 17s\tremaining: 2m 58s\n","891:\tlearn: 0.1910028\ttotal: 24m 19s\tremaining: 2m 56s\n","892:\tlearn: 0.1908055\ttotal: 24m 20s\tremaining: 2m 55s\n","893:\tlearn: 0.1906709\ttotal: 24m 22s\tremaining: 2m 53s\n","894:\tlearn: 0.1904239\ttotal: 24m 23s\tremaining: 2m 51s\n","895:\tlearn: 0.1903178\ttotal: 24m 25s\tremaining: 2m 50s\n","896:\tlearn: 0.1901461\ttotal: 24m 27s\tremaining: 2m 48s\n","897:\tlearn: 0.1899763\ttotal: 24m 28s\tremaining: 2m 46s\n","898:\tlearn: 0.1897550\ttotal: 24m 30s\tremaining: 2m 45s\n","899:\tlearn: 0.1895780\ttotal: 24m 32s\tremaining: 2m 43s\n","900:\tlearn: 0.1894037\ttotal: 24m 33s\tremaining: 2m 41s\n","901:\tlearn: 0.1891120\ttotal: 24m 35s\tremaining: 2m 40s\n","902:\tlearn: 0.1889558\ttotal: 24m 36s\tremaining: 2m 38s\n","903:\tlearn: 0.1888947\ttotal: 24m 38s\tremaining: 2m 37s\n","904:\tlearn: 0.1887140\ttotal: 24m 40s\tremaining: 2m 35s\n","905:\tlearn: 0.1885149\ttotal: 24m 41s\tremaining: 2m 33s\n","906:\tlearn: 0.1883260\ttotal: 24m 43s\tremaining: 2m 32s\n","907:\tlearn: 0.1881430\ttotal: 24m 45s\tremaining: 2m 30s\n","908:\tlearn: 0.1879554\ttotal: 24m 46s\tremaining: 2m 28s\n","909:\tlearn: 0.1878088\ttotal: 24m 48s\tremaining: 2m 27s\n","910:\tlearn: 0.1876606\ttotal: 24m 49s\tremaining: 2m 25s\n","911:\tlearn: 0.1874257\ttotal: 24m 51s\tremaining: 2m 23s\n","912:\tlearn: 0.1872912\ttotal: 24m 53s\tremaining: 2m 22s\n","913:\tlearn: 0.1871334\ttotal: 24m 54s\tremaining: 2m 20s\n","914:\tlearn: 0.1870174\ttotal: 24m 56s\tremaining: 2m 19s\n","915:\tlearn: 0.1867706\ttotal: 24m 57s\tremaining: 2m 17s\n","916:\tlearn: 0.1864399\ttotal: 24m 59s\tremaining: 2m 15s\n","917:\tlearn: 0.1862177\ttotal: 25m 1s\tremaining: 2m 14s\n","918:\tlearn: 0.1860091\ttotal: 25m 2s\tremaining: 2m 12s\n","919:\tlearn: 0.1858599\ttotal: 25m 4s\tremaining: 2m 10s\n","920:\tlearn: 0.1857341\ttotal: 25m 6s\tremaining: 2m 9s\n","921:\tlearn: 0.1856955\ttotal: 25m 7s\tremaining: 2m 7s\n","922:\tlearn: 0.1854925\ttotal: 25m 9s\tremaining: 2m 5s\n","923:\tlearn: 0.1853267\ttotal: 25m 10s\tremaining: 2m 4s\n","924:\tlearn: 0.1851851\ttotal: 25m 12s\tremaining: 2m 2s\n","925:\tlearn: 0.1850736\ttotal: 25m 14s\tremaining: 2m 1s\n","926:\tlearn: 0.1848207\ttotal: 25m 15s\tremaining: 1m 59s\n","927:\tlearn: 0.1846271\ttotal: 25m 17s\tremaining: 1m 57s\n","928:\tlearn: 0.1841831\ttotal: 25m 19s\tremaining: 1m 56s\n","929:\tlearn: 0.1840626\ttotal: 25m 20s\tremaining: 1m 54s\n","930:\tlearn: 0.1838944\ttotal: 25m 22s\tremaining: 1m 52s\n","931:\tlearn: 0.1836846\ttotal: 25m 23s\tremaining: 1m 51s\n","932:\tlearn: 0.1834033\ttotal: 25m 25s\tremaining: 1m 49s\n","933:\tlearn: 0.1829630\ttotal: 25m 27s\tremaining: 1m 47s\n","934:\tlearn: 0.1826603\ttotal: 25m 28s\tremaining: 1m 46s\n","935:\tlearn: 0.1824709\ttotal: 25m 30s\tremaining: 1m 44s\n","936:\tlearn: 0.1824347\ttotal: 25m 32s\tremaining: 1m 43s\n","937:\tlearn: 0.1821755\ttotal: 25m 33s\tremaining: 1m 41s\n","938:\tlearn: 0.1820303\ttotal: 25m 35s\tremaining: 1m 39s\n","939:\tlearn: 0.1819434\ttotal: 25m 36s\tremaining: 1m 38s\n","940:\tlearn: 0.1818378\ttotal: 25m 38s\tremaining: 1m 36s\n","941:\tlearn: 0.1816194\ttotal: 25m 40s\tremaining: 1m 34s\n","942:\tlearn: 0.1814523\ttotal: 25m 41s\tremaining: 1m 33s\n","943:\tlearn: 0.1814200\ttotal: 25m 41s\tremaining: 1m 31s\n","944:\tlearn: 0.1812511\ttotal: 25m 43s\tremaining: 1m 29s\n","945:\tlearn: 0.1811314\ttotal: 25m 45s\tremaining: 1m 28s\n","946:\tlearn: 0.1808435\ttotal: 25m 46s\tremaining: 1m 26s\n","947:\tlearn: 0.1806517\ttotal: 25m 48s\tremaining: 1m 24s\n","948:\tlearn: 0.1804825\ttotal: 25m 50s\tremaining: 1m 23s\n","949:\tlearn: 0.1803516\ttotal: 25m 51s\tremaining: 1m 21s\n","950:\tlearn: 0.1802691\ttotal: 25m 53s\tremaining: 1m 20s\n","951:\tlearn: 0.1801050\ttotal: 25m 54s\tremaining: 1m 18s\n","952:\tlearn: 0.1799940\ttotal: 25m 56s\tremaining: 1m 16s\n","953:\tlearn: 0.1798395\ttotal: 25m 58s\tremaining: 1m 15s\n","954:\tlearn: 0.1795994\ttotal: 25m 59s\tremaining: 1m 13s\n","955:\tlearn: 0.1792725\ttotal: 26m 1s\tremaining: 1m 11s\n","956:\tlearn: 0.1791256\ttotal: 26m 3s\tremaining: 1m 10s\n","957:\tlearn: 0.1789739\ttotal: 26m 4s\tremaining: 1m 8s\n","958:\tlearn: 0.1787508\ttotal: 26m 6s\tremaining: 1m 6s\n","959:\tlearn: 0.1784902\ttotal: 26m 7s\tremaining: 1m 5s\n","960:\tlearn: 0.1780138\ttotal: 26m 9s\tremaining: 1m 3s\n","961:\tlearn: 0.1778721\ttotal: 26m 11s\tremaining: 1m 2s\n","962:\tlearn: 0.1775933\ttotal: 26m 12s\tremaining: 1m\n","963:\tlearn: 0.1774695\ttotal: 26m 14s\tremaining: 58.8s\n","964:\tlearn: 0.1773177\ttotal: 26m 16s\tremaining: 57.2s\n","965:\tlearn: 0.1771093\ttotal: 26m 17s\tremaining: 55.5s\n","966:\tlearn: 0.1769700\ttotal: 26m 19s\tremaining: 53.9s\n","967:\tlearn: 0.1767406\ttotal: 26m 20s\tremaining: 52.3s\n","968:\tlearn: 0.1765641\ttotal: 26m 22s\tremaining: 50.6s\n","969:\tlearn: 0.1763216\ttotal: 26m 24s\tremaining: 49s\n","970:\tlearn: 0.1761156\ttotal: 26m 25s\tremaining: 47.4s\n","971:\tlearn: 0.1758804\ttotal: 26m 27s\tremaining: 45.7s\n","972:\tlearn: 0.1757421\ttotal: 26m 29s\tremaining: 44.1s\n","973:\tlearn: 0.1755219\ttotal: 26m 30s\tremaining: 42.5s\n","974:\tlearn: 0.1750257\ttotal: 26m 32s\tremaining: 40.8s\n","975:\tlearn: 0.1748501\ttotal: 26m 34s\tremaining: 39.2s\n","976:\tlearn: 0.1746901\ttotal: 26m 35s\tremaining: 37.6s\n","977:\tlearn: 0.1745155\ttotal: 26m 37s\tremaining: 35.9s\n","978:\tlearn: 0.1743950\ttotal: 26m 38s\tremaining: 34.3s\n","979:\tlearn: 0.1742059\ttotal: 26m 40s\tremaining: 32.7s\n","980:\tlearn: 0.1739696\ttotal: 26m 42s\tremaining: 31s\n","981:\tlearn: 0.1737604\ttotal: 26m 43s\tremaining: 29.4s\n","982:\tlearn: 0.1735839\ttotal: 26m 45s\tremaining: 27.8s\n","983:\tlearn: 0.1733036\ttotal: 26m 46s\tremaining: 26.1s\n","984:\tlearn: 0.1730803\ttotal: 26m 48s\tremaining: 24.5s\n","985:\tlearn: 0.1727599\ttotal: 26m 50s\tremaining: 22.9s\n","986:\tlearn: 0.1726635\ttotal: 26m 51s\tremaining: 21.2s\n","987:\tlearn: 0.1726059\ttotal: 26m 53s\tremaining: 19.6s\n","988:\tlearn: 0.1723170\ttotal: 26m 55s\tremaining: 18s\n","989:\tlearn: 0.1721414\ttotal: 26m 56s\tremaining: 16.3s\n","990:\tlearn: 0.1720092\ttotal: 26m 58s\tremaining: 14.7s\n","991:\tlearn: 0.1719152\ttotal: 26m 59s\tremaining: 13.1s\n","992:\tlearn: 0.1716365\ttotal: 27m 1s\tremaining: 11.4s\n","993:\tlearn: 0.1714105\ttotal: 27m 3s\tremaining: 9.8s\n","994:\tlearn: 0.1713133\ttotal: 27m 4s\tremaining: 8.16s\n","995:\tlearn: 0.1712465\ttotal: 27m 6s\tremaining: 6.53s\n","996:\tlearn: 0.1710937\ttotal: 27m 8s\tremaining: 4.9s\n","997:\tlearn: 0.1709077\ttotal: 27m 9s\tremaining: 3.27s\n","998:\tlearn: 0.1707755\ttotal: 27m 11s\tremaining: 1.63s\n","999:\tlearn: 0.1706358\ttotal: 27m 12s\tremaining: 0us\n"]},{"data":{"text/plain":["\u003ccatboost.core.CatBoostRegressor at 0x7f0398d9f9d0\u003e"]},"execution_count":null,"metadata":{},"output_type":"execute_result"}],"source":["#Fitting catboost regressor model on the train data\n","cat = CatBoostRegressor(random_state = 123  , max_depth = 14 )\n","cat.fit(train_final , y)"]},{"cell_type":"markdown","metadata":{"id":"iH4sY6GAwJ5Z"},"source":["#### Prediction"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"CkeWLC1CwJ5Z"},"outputs":[],"source":["#Combine the prediction of all the model\n","#Weighatages are assigned to model based on trial and error method\n","ypred = (( 0.05 * rf.predict(test_final) + 0.1 * xgb.predict(test_final) + 0.45 *  etr.predict(test_final) +\n","          0.1 * cat.predict(test_final) +\n","          0.3 * lgbm.predict(test_final) ))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"joDoJPLrwJ5a"},"outputs":[],"source":["#Dump the prediction output\n","ypred1 = pd.DataFrame( np.exp(ypred) )\n","ypred1.columns = ['Price']\n","ypred1.to_csv('final_submission.csv',index = False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aPdhJFKwwJ5a"},"outputs":[],"source":[""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6_hPTDdVwJ5a"},"outputs":[],"source":[""]}],"metadata":{"colab":{"collapsed_sections":[],"name":"Final_solution_hackathon.ipynb","version":""},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.3"},"latex_envs":{"LaTeX_envs_menu_present":true,"autoclose":false,"autocomplete":true,"bibliofile":"biblio.bib","cite_by":"apalike","current_citInitial":1,"eqLabelWithNumbers":true,"eqNumInitial":1,"hotkeys":{"equation":"Ctrl-E","itemize":"Ctrl-I"},"labels_anchors":false,"latex_user_defs":false,"report_style_numbering":false,"user_envs_cfg":false},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":false,"toc_position":{},"toc_section_display":true,"toc_window_display":false}},"nbformat":4,"nbformat_minor":0}